(Towards) Scalable Reliable Automated Evaluation with Large Language Models

# (Towards) Scalable Reliable Automated Evaluation with Large Language Models
Source: [https://arxiv.org/html/2607.28282](https://arxiv.org/html/2607.28282)
###### Abstract

Evaluating the quality and relevance of textual outputs from Large Language Models \(LLMs\) remains challenging and resource\-intensive\. Existing automated metrics often fail to capture the complexity and variability inherent in LLM\-generated outputs\. Moreover, these metrics typically rely on explicit reference standards, limiting their use mostly to domains with objective benchmarks\. This work introduces a novel evaluation framework designed to approximate expert\-level assessments of LLM\-generated content\. The proposed method employs pairwise comparisons of outputs by multiple LLMs, reducing biases from individual models\. An Elo rating system is used to generate stable and interpretable rankings\. Adjustable agreement thresholds—from full unanimity to majority voting—allow flexible control over evaluation confidence and coverage\. The method’s effectiveness is demonstrated through evaluating competency profiles extracted from scientific abstracts\. Preliminary results show that automatically derived rankings correlate well with expert judgments, significantly reducing the need for extensive human intervention\. By offering a scalable, consistent, and domain\-agnostic evaluation layer, the framework supports more efficient and reliable quality assessments of LLM outputs across diverse applications\.

AIArtificial IntelligenceAPIApplication Programming InterfaceDQData QualityMLMachine LearningLLMLarge Language ModelLMMLarge Multimodal ModelLPMLarge Process ModelNLPNatural Language ProcessingGPTGenerative Pretrained TransformerGPT ModelGenerative Pretrained Transformer ModelRNNRecurrent Neural NetworkLSTMLong Short\-term Memory networkGRUGated Recurrent UnitBPMBusiness Process ManagementBPMNBusiness Process Model and NotationOCROptical Character RecognitionGPUGraphical Processing UnitRAGRetrieval\-Augmented GenerationSFTSupervised Fine\-TuningHITLHuman\-in\-the\-LoopRLHFReinforcement Learning from Human FeedbackDPODirect Preference OptimizationPEFTParameter\-Efficient Fine\-TuningLoRALow\-rank adaptationQLoRAQuantized Low\-rank adaptationSOTAState\-of\-the\-ArtCoTChain of ThoughtMMLUMassive Multi\-task Language Understanding
\(Towards\) Scalable Reliable Automated Evaluation with Large Language Models

Bertil BraunKITbertil\.braun@alumni\.kit\.eduMartin ForellKITmartin\.forell@kit\.edu

## 1Introduction

[Large Language Models](https://arxiv.org/html/2607.28282#id5.5.id5)are machine learning\-based models capable of understanding, analyzing, and generating human languageJarrahiet al\.\([2023](https://arxiv.org/html/2607.28282#bib.bib51)\)\. Their advanced capabilities stem from extensive training on large\-scale datasets, enabling them to develop a profound understanding of syntax, semantics, and contextual language aspectsChanget al\.\([2024](https://arxiv.org/html/2607.28282#bib.bib90)\)\. Consequently, natural language processing has become a core component of[LLMs](https://arxiv.org/html/2607.28282#id5.5.id5)\. Recent advancements have significantly improved their capacity for semantic analysis and textual data comprehensionDeutschet al\.\([2021](https://arxiv.org/html/2607.28282#bib.bib38)\); Wuet al\.\([2023](https://arxiv.org/html/2607.28282#bib.bib58)\)\. As a result,[LLMs](https://arxiv.org/html/2607.28282#id5.5.id5)are broadly employed across numerous domains, including software test generationSchäferet al\.\([2024](https://arxiv.org/html/2607.28282#bib.bib73)\), question answeringLianget al\.\([2023](https://arxiv.org/html/2607.28282#bib.bib67)\), and text summarizationDeutschet al\.\([2021](https://arxiv.org/html/2607.28282#bib.bib38)\); Puet al\.\([2023](https://arxiv.org/html/2607.28282#bib.bib70)\)\.

Evaluating the quality of textual outputs generated by[LLMs](https://arxiv.org/html/2607.28282#id5.5.id5), however, poses significant methodological challenges, primarily due to the inherently subjective and task\-specific nature of text evaluationAnwaret al\.\([2024](https://arxiv.org/html/2607.28282#bib.bib76)\); Changet al\.\([2024](https://arxiv.org/html/2607.28282#bib.bib90)\)\. Traditional evaluation approaches typically depend on either human judgment—which is resource\-intensive, inconsistent, and difficult to scale—or predefined metrics that are often insufficient to capture nuanced variations in quality across diverse tasksChiang and Lee \([2023](https://arxiv.org/html/2607.28282#bib.bib52)\)\. These limitations highlight a critical gap in current evaluation methodologies, underscoring the necessity for more robust and scalable alternatives\.

To address these evaluation challenges, this paper proposes a robust and scalable evaluation framework that leverages[LLMs](https://arxiv.org/html/2607.28282#id5.5.id5)themselves to perform systematic pairwise comparisons\. In contrast to conventional methods dependent solely on single\-[LLM](https://arxiv.org/html/2607.28282#id5.5.id5)judgments or fixed metrics, the presented approach integrates multiple[LLM](https://arxiv.org/html/2607.28282#id5.5.id5)judgments and aggregates them using the Elo rating system\. This aggregation method produces reliable and consistent rankings, substantially reducing the need for extensive human evaluation\. Thus, the proposed method serves effectively as a universal evaluation layer applicable to a wide range of tasks involving free\-form text generation\.

The remainder of this paper is structured as follows: Section[2](https://arxiv.org/html/2607.28282#S2)introduces foundational concepts, including[LLMs](https://arxiv.org/html/2607.28282#id5.5.id5), the Elo rating system, and correlation metrics\. Section[3](https://arxiv.org/html/2607.28282#S3)provides an overview of related work\. Section[4](https://arxiv.org/html/2607.28282#S4)describes the proposed evaluation framework in detail, followed by a prototypical implementation in Section[5](https://arxiv.org/html/2607.28282#S5)\. Section[6](https://arxiv.org/html/2607.28282#S6)demonstrates the framework’s applicability by evaluating its performance in extracting competency profiles from scientific abstracts and discusses the results\. Section[7](https://arxiv.org/html/2607.28282#S7)summarizes the main contributions and concludes the paper\. Finally, Section[8](https://arxiv.org/html/2607.28282#S8)highlights the limitations of the proposed approach\.

## 2Background

This section briefly introduces foundational concepts of[LLMs](https://arxiv.org/html/2607.28282#id5.5.id5), the Elo rating system, and correlation metrics, which are essential for understanding the proposed evaluation framework presented subsequently\.

### 2\.1Large Language Models

[LLMs](https://arxiv.org/html/2607.28282#id5.5.id5)have transformed[Natural Language Processing](https://arxiv.org/html/2607.28282#id8.8.id8)\([NLP](https://arxiv.org/html/2607.28282#id8.8.id8)\) through advanced machine learning methods, particularly the Transformer architecture, which efficiently captures long\-range dependencies via self\-attention mechanismsVaswaniet al\.\([2023](https://arxiv.org/html/2607.28282#bib.bib66)\)\. Modern[LLMs](https://arxiv.org/html/2607.28282#id5.5.id5), such as GPT\-4oOpenAIet al\.\([2024](https://arxiv.org/html/2607.28282#bib.bib84)\), Llama 3MetaAI \([2024](https://arxiv.org/html/2607.28282#bib.bib79)\), MistralJianget al\.\([2023](https://arxiv.org/html/2607.28282#bib.bib69)\), and Phi 3Abdinet al\.\([2024](https://arxiv.org/html/2607.28282#bib.bib80)\), represent the state of the art in diverse[NLP](https://arxiv.org/html/2607.28282#id8.8.id8)tasks, leveraging extensive pre\-training on vast textual datasets\.

To further enhance the quality and contextual appropriateness of outputs, various prompt engineering methods have emerged, notablyRole PromptingWanget al\.\([2024](https://arxiv.org/html/2607.28282#bib.bib82)\),Knowledge InjectionMartinoet al\.\([2023](https://arxiv.org/html/2607.28282#bib.bib57)\), and[Chain of Thought](https://arxiv.org/html/2607.28282#id27.27.id27)\([CoT](https://arxiv.org/html/2607.28282#id27.27.id27)\)Weiet al\.\([2023](https://arxiv.org/html/2607.28282#bib.bib53)\)\. Additionally, the[Retrieval\-Augmented Generation](https://arxiv.org/html/2607.28282#id18.18.id18)\([RAG](https://arxiv.org/html/2607.28282#id18.18.id18)\) approachLewiset al\.\([2021](https://arxiv.org/html/2607.28282#bib.bib37)\)integrates retrieval mechanisms into text generation, allowing[LLMs](https://arxiv.org/html/2607.28282#id5.5.id5)to dynamically incorporate external domain\-specific knowledge, thereby improving accuracy and relevance without extensive retraining\.

### 2\.2Elo Rating System for Ranking Items

The Elo rating systemElo \([1986](https://arxiv.org/html/2607.28282#bib.bib13)\), originally developed to rank chess players based on their relative skill levels, is a method for dynamically updating item rankings through pairwise comparisons\. Each item begins with an initial rating \(e\.g\., 1000 points\), which is adjusted after every comparison\.

The Elo system uses the following formula to calculate the expected score for an item:

E=11\+10\(Ratingopponent−Ratingplayer\)/400E=\\frac\{1\}\{1\+10^\{\(\\text\{Rating\}\_\{\\text\{opponent\}\}\-\\text\{Rating\}\_\{\\text\{player\}\}\)/400\}\}whereEErepresents the expected probability of an item winning against its opponent\. After a comparison, the rating is updated as:

Ratingnew=Ratingcurrent\+K×\(Score−E\)\\text\{Rating\}\_\{\\text\{new\}\}=\\text\{Rating\}\_\{\\text\{current\}\}\+K\\times\(\\text\{Score\}\-E\)whereKKis a constant \(typically 4 \- 32\) that determines the magnitude of rating adjustments, andScoreis1\.01\.0for a win,0\.00\.0for a loss, and0\.50\.5for a draw\.

By iterating this process across all pairwise outcomes, the Elo system produces a final ranked list of items\. Items with consistently strong performance rise in rank, while those with frequent losses fall\. This dynamic ranking approach ensures that the final rankings are both robust and reflective of the relative quality of the items\.

### 2\.3Correlation Metrics

To assess agreement between automated evaluations and expert judgments, correlation metrics specifically suited for ordinal data are necessary\. Spearman’s rank correlation coefficient \(Spearman’sρ\\rho\) measures the strength and direction of monotonic relationships between two ranked variables by comparing ranks rather than absolute valuesSpearman \([2010](https://arxiv.org/html/2607.28282#bib.bib26)\)\. Kendall’s tau \(τ\\tau\) similarly assesses rank correlation, but relies on pairwise comparisons, quantifying the proportion of concordant versus discordant rank pairsKendall \([1938](https://arxiv.org/html/2607.28282#bib.bib12)\)\. Both metrics range from−1\-1to\+1\+1, where values near\+1\+1indicate strong positive agreement, near−1\-1imply strong disagreement, and values close to0suggest minimal or no correlation\. They do not assume linear relationships or normal distributions, making them particularly robust for evaluating ranked data in experimental settings\.

## 3Related Work

Evaluation of[LLMs](https://arxiv.org/html/2607.28282#id5.5.id5)has become increasingly crucial due to their widespread application\. Reliable assessment methods are necessary to ensure outputs meet quality standards, motivating the development of various evaluation strategies\. Existing methodologies typically fall into two categories:reference\-based metricsandreference\-free methods\.

Reference\-based metrics, such asBLEUPapineniet al\.\([2002](https://arxiv.org/html/2607.28282#bib.bib17)\),ROUGELin \([2004](https://arxiv.org/html/2607.28282#bib.bib18)\), andBERTScoreZhanget al\.\([2020](https://arxiv.org/html/2607.28282#bib.bib32)\), assess outputs by comparing them to predefined reference texts\. However, their dependence on static references limits their applicability, especially for creative or open\-ended tasksChanget al\.\([2024](https://arxiv.org/html/2607.28282#bib.bib90)\)\.

To overcome this limitation, reference\-free methods likeGPTScoreFuet al\.\([2023](https://arxiv.org/html/2607.28282#bib.bib63)\)have emerged, directly evaluating outputs based on token probabilities and task\-specific dimensions\. Although these approaches are promising, they sometimes exhibit limited correlation with human judgmentsFuet al\.\([2023](https://arxiv.org/html/2607.28282#bib.bib63)\), highlighting the ongoing need for more accurate evaluation techniques\.

An alternative approach, known asLLM\-as\-a\-JudgeZhenget al\.\([2023](https://arxiv.org/html/2607.28282#bib.bib68)\), utilizes[LLMs](https://arxiv.org/html/2607.28282#id5.5.id5)themselves to perform evaluations\. This can be implemented either through single\-[LLM](https://arxiv.org/html/2607.28282#id5.5.id5)scoring or through more robust multi\-[LLM](https://arxiv.org/html/2607.28282#id5.5.id5)frameworks, such as debates or peer reviewsChanget al\.\([2024](https://arxiv.org/html/2607.28282#bib.bib90)\); Lianget al\.\([2023](https://arxiv.org/html/2607.28282#bib.bib67)\)\.

Within multi\-[LLM](https://arxiv.org/html/2607.28282#id5.5.id5)evaluation frameworks, the Elo rating system has gained popularity as a structured method for dynamically ranking models based on pairwise comparisons\. Despite its widespread use, Elo ratings are sensitive to factors such as evaluation order and hyperparameter selection, leading to reliability concernsBoubdiret al\.\([2023](https://arxiv.org/html/2607.28282#bib.bib55)\)\. Recent work byBoubdiret al\.\([2023](https://arxiv.org/html/2607.28282#bib.bib55)\)proposes guidelines to enhance reliability, including a permutation oversampling approach to mitigate order effects, thereby enabling a more robust and dependable model performance assessment\.

## 4Approach

Overview and Motivation\.This section presents a methodology for utilizing[LLMs](https://arxiv.org/html/2607.28282#id5.5.id5)to assess diverse free\-text responses to a given task \(e\.g\., summarization\) through a pairwise comparison methodology\. Evaluating free\-text outputs with[LLMs](https://arxiv.org/html/2607.28282#id5.5.id5)poses several challenges:

- •C1 – Subjectivity in Scoring:Absolute scores are often inconsistent and subject to scaling issues\.
- •C2 – LLM Biases:Positional, verbosity, and stylistic biases can distort evaluation outcomes\.
- •C3 – Handling Multiple Evaluations:Aggregating multiple LLM outputs into a coherent decision is non\-trivial\.
- •C4 – Robust Ranking:Deriving a definitive ordering of items in a bias\-minimized fashion requires a resilient aggregation mechanism\.

To address these challenges, our pipeline is organized into three distinct stages: \(I\) generation of items to compare, \(II\) systematic pairwise comparison using multiple[LLMs](https://arxiv.org/html/2607.28282#id5.5.id5), and \(III\) ranking the items with an Elo rating system to clearly identify the best\-performing candidates\. Figure[1](https://arxiv.org/html/2607.28282#S4.F1)outlines this pipeline\.

![Refer to caption](https://arxiv.org/html/2607.28282v1/x1.png)Figure 1:Pipeline Overview: A three\-stage methodology including Generation, Comparison, and Ranking\.The methodology begins with generating multiple items intended for comparison\. This step may include various methodologies to ensure diverse inputs for evaluation\. For example, different hyperparameter configurations, distinct[LLMs](https://arxiv.org/html/2607.28282#id5.5.id5), or alternative wording styles can be employed\. The methodology then systematically assesses and ranks the items, enabling the identification of the methodology with the best results for the given task\.

Typical applications include hyperparameter optimization, method comparison, and[LLM](https://arxiv.org/html/2607.28282#id5.5.id5)selection, where the objective is to determine the most effective configuration or[LLM](https://arxiv.org/html/2607.28282#id5.5.id5)\.

Based on the final Elo ranking, the performance of different methods is assessed, and the best\-performing item is identified\. This highest\-ranked item can subsequently be deployed in production environments or research settings\.

### 4\.1Pairwise Comparison Framework

![Refer to caption](https://arxiv.org/html/2607.28282v1/x2.png)Figure 2:Overview of the message prompt used in the evaluation methodology\.\(Addresses C1 – Subjectivity in Scoring\)The evaluation methodology builds upon a pairwise comparison methodology designed to deliver precise and consistent evaluations\. Instead of assigning absolute scores—which are susceptible to subjectivity and scaling inconsistenciesLiuet al\.\([2025](https://arxiv.org/html/2607.28282#bib.bib101)\); Guet al\.\([2025](https://arxiv.org/html/2607.28282#bib.bib104)\)—the focus lies on relative judgments through direct item\-to\-item comparisons\. Two items are presented simultaneously to an[LLM](https://arxiv.org/html/2607.28282#id5.5.id5), with evaluation criteria explicitly defined by the user based on the specific task\. For instance, in summarization tasks, the criterion might be: "which item better summarizes a given text?"\.

The methodology incorporates established[State\-of\-the\-Art](https://arxiv.org/html/2607.28282#id26.26.id26)\([SOTA](https://arxiv.org/html/2607.28282#id26.26.id26)\) prompting techniques, including Role Prompting,[CoT](https://arxiv.org/html/2607.28282#id27.27.id27), and Self\-Consistency Decoding, to optimize performance, enhance consistency, and mitigate biases from[LLMs](https://arxiv.org/html/2607.28282#id5.5.id5)\. The evaluation methodology leverages[RAG](https://arxiv.org/html/2607.28282#id18.18.id18)by embedding contextually relevant examples directly into the prompt, effectively creating a few\-shot scenarioBrownet al\.\([2020](https://arxiv.org/html/2607.28282#bib.bib34)\)\. These examples illustrate appropriate evaluation practices, providing clear task demonstrations \(see Figure[2](https://arxiv.org/html/2607.28282#S4.F2)\)\. A complete example of the prompt structure used in our evaluation framework is provided in Appendix[A\.3](https://arxiv.org/html/2607.28282#A1.SS3)\. The prompt requires the[LLM](https://arxiv.org/html/2607.28282#id5.5.id5)to engage in a chain\-of\-thought process, articulating its reasoning step\-by\-step before deciding on the item that best aligns with the specified criteria\. Additionally, a system prompt ensures a structured output format, systematically presenting detailed reasoning alongside the final decision\. This integrated strategy ensures systematic, transparent, and goal\-aligned evaluations, supporting robust downstream analyses\.

All pairwise comparison outcomes are fully automated\. Once the prompts and task\-specific evaluation criteria are defined, no human judgment is involved in determining which item wins a given comparison\. Each[LLM](https://arxiv.org/html/2607.28282#id5.5.id5)receives the same structured prompt with fixed instructions and examples, and the final rankings are derived solely from the aggregated Elo updates based on these model judgments\.

Givennnitems, the total number of unique comparisons isn×\(n−1\)2\\frac\{n\\times\(n\-1\)\}\{2\}\. To mitigate positional biases, each pair is evaluated bidirectionally — posing both questions: "Is A better than B?" and "Is B better than A?" to the[LLMs](https://arxiv.org/html/2607.28282#id5.5.id5)\. This strategy ensures that evaluation results remain independent of item presentation order\. Additionally, multiple[LLMs](https://arxiv.org/html/2607.28282#id5.5.id5)are utilized for each comparison, significantly enhancing the robustness of the methodology\. Fornnitems evaluated byNLLMN\_\{\\mathrm\{LLM\}\}[LLMs](https://arxiv.org/html/2607.28282#id5.5.id5), the total number of pairwise evaluations isn×\(n−1\)×NLLMn\\times\(n\-1\)\\times N\_\{\\mathrm\{LLM\}\}\.

To ensure consistency, all[LLMs](https://arxiv.org/html/2607.28282#id5.5.id5)receive identical prompts, and the evaluation criteria remain fixed\.

### 4\.2Mitigation of LLM Biases

\(Addresses C2 – LLM Biases\)[LLMs](https://arxiv.org/html/2607.28282#id5.5.id5)exhibit various biases that can compromise the reliability of evaluations\. Positional bias is one prominent issue, with[LLMs](https://arxiv.org/html/2607.28282#id5.5.id5)often favoring the last\-presented option in pairwise comparisons, as highlighted byZhaoet al\.\([2025](https://arxiv.org/html/2607.28282#bib.bib103)\)\. Additionally, verbosity bias, which favors longer or more elaborate responses regardless of quality, is commonZhaoet al\.\([2021](https://arxiv.org/html/2607.28282#bib.bib35)\)\. Stylistic biases, including preferences for particular syntactic structures or formality, also potentially skew evaluations involving language variationLewkowyczet al\.\([2022](https://arxiv.org/html/2607.28282#bib.bib44)\)\. If unaddressed, these biases can introduce systematic errors into evaluations\.

To mitigate these biases, several strategies are incorporated:

First, prompts are meticulously crafted using neutral and unbiased language to avoid unintentionally influencing the[LLM](https://arxiv.org/html/2607.28282#id5.5.id5)’s judgment\. Furthermore, prompt consistency across evaluations minimizes variability arising from prompt design\.

Second, bidirectional evaluations counter positional bias by reversing the presentation order of items in comparisons, thereby reducing order\-induced preferences\.

Third,[RAG](https://arxiv.org/html/2607.28282#id18.18.id18)techniques are utilized\. Given a database containing relevant examples from previous evaluations \(for example, expert\-reviewed domain\-specific comparisons\), the most contextually similar example is retrieved and included in the prompt\. This provides the[LLM](https://arxiv.org/html/2607.28282#id5.5.id5)with concrete demonstrations of previously applied criteria in similar contexts, enabling more informed, criteria\-consistent evaluations and reducing potential biases through recognizing patterns and contextual commonalities\.

### 4\.3Handling Multiple Evaluations and Agreement Thresholds

\(Addresses C3 – Handling Multiple Evaluations\)When multiple[LLMs](https://arxiv.org/html/2607.28282#id5.5.id5)are used to evaluate item pairs, it is necessary to reconcile potentially differing judgments in a principled way\. We explore two main strategies for aggregating multiple outputs: \(1\) threshold\-based consensus, and \(2\) individual updates without aggregation\.

In the threshold\-based setting, an agreement threshold is specified \(e\.g\., 100%, 90%, 75%, 50%\) that determines whether a pairwise judgment results in a win/loss or a draw\. These thresholds correspond to intuitive decision modes:*consensus*\(1\.0\),*near\-consensus*\(0\.9\),*qualified majority*\(0\.75\), and*simple majority*\(0\.5\)\. If the specified threshold is met—for example, at least 75% of[LLMs](https://arxiv.org/html/2607.28282#id5.5.id5)agree that item A is better than item B—an Elo update is performed accordingly\. Otherwise, the comparison is treated as a draw, resulting in no net change in Elo scores\. Higher thresholds prioritize certainty but produce more draws and may limit informativeness; thresholds below 0\.5, in contrast, allow minority judgments to dominate and are typically avoided\.

An alternative approach is to treat each[LLM](https://arxiv.org/html/2607.28282#id5.5.id5)’s judgment independently, applying Elo updates for each evaluation\. Rather than collapsing multiple judgments into a single binary decision, this method—termed the*No\-Threshold*variant—aggregates signal proportionally\. For example, if 80% of[LLMs](https://arxiv.org/html/2607.28282#id5.5.id5)prefer A over B, the cumulative updates represent a net 60% push in favor of A, without discarding minority votes or reducing the result to a draw\. This method retains more of the available information and avoids the over\-conservatism introduced by strict agreement requirements\.

### 4\.4Elo Rating System for Ranking Items

\(Addresses C4 – Robust Ranking\)Following the completion of pairwise comparisons, the results are aggregated into a global ranking using an Elo rating system \(see[2\.2](https://arxiv.org/html/2607.28282#S2.SS2)\), producing a definitive ordered list of items\. The Elo system is particularly suitable due to its dynamic updating mechanism based on pairwise outcomes\. Items with consistently positive outcomes improve in ranking, while those frequently losing decline\. This iterative method ensures final rankings robustly and accurately reflect the relative quality of the evaluated items\. In addition, we hereby adhere to the guidelines proposed byBoubdiret al\.\([2023](https://arxiv.org/html/2607.28282#bib.bib55)\), by sampling multiple permutations of the LLM evaluations and applying the Elo rating system to each permutation\. This approach ensures that the final ranking is not overly influenced by any single evaluation order, enhancing robustness and reliability\.

##### Interpretability of Elo scores\.

A convenient property of Elo is that a score*difference*\(Δ\\Delta\) maps directly to an expected win\-probability via

P​\(A beats B\)=11\+10−Δ/400\.P\(\\text\{A beats B\}\)=\\frac\{1\}\{1\+10^\{\-\\Delta/400\}\}\.For example,Δ=100\\Delta=100implies that item A should win about64%64\\,\\%of head\-to\-head comparisons with item B, whereasΔ=200\\Delta=200raises that expectation to roughly76%76\\,\\%\. We therefore encourage practitioners to report not only the final rank ordering but also the Elo gaps between adjacent candidates\. A task\-agnostic rule\-of\-thumb is:

- •Δ<50\\Delta<50pts — items are practically tied;
- •50≤Δ≤15050\\leq\\Delta\\leq 150pts — a noticeable but moderate quality gap;
- •Δ\>150\\Delta\>150pts — a strong, user\-perceivable difference\.

Publishing these gaps alongside ranks helps downstream readers understand*how much better*one output is expected to be, not merely*which*one is on top\.

## 5Implementation

The proposed evaluation pipeline has been implemented and is demonstrated through a specific use case: generating competency profiles from research abstracts \(see Section[5\.1](https://arxiv.org/html/2607.28282#S5.SS1)\)\. This scenario illustrates how the framework can be applied to real\-world data and highlights its effectiveness in evaluating complex, task\-specific outputs\. Competency profiles serve as a concrete example of evaluable items throughout the following sections\. The implementation leverages widely adopted tools and frameworks to ensure scalability, usability, and reliability\. This section details the technical stack, the integration of[LLMs](https://arxiv.org/html/2607.28282#id5.5.id5), data sources, and the experimental setup\. Additionally, it discusses implementation challenges and the strategies used to address them\.

### 5\.1Structured Competency Profiles

A competency profile is defined as a structured summary of the research capabilities demonstrated by the authors of a given set of academic papers\. Specifically, it identifies the overarching research domain in which the authors operate, alongside a set of 5 to 8 competencies that reflect key areas of expertise\. Each competency is accompanied by a brief description \(1–2 sentences\) outlining its scope and relevance \(see Appendix[A\.2](https://arxiv.org/html/2607.28282#A1.SS2)for examples\)\. To generate such profiles, a[LLM](https://arxiv.org/html/2607.28282#id5.5.id5)is prompted with the abstracts of the input papers and tasked with inferring both the general domain and the detailed competencies exhibited across the works\.

To evaluate the accuracy of these generated profiles, the evaluation[LLMs](https://arxiv.org/html/2607.28282#id5.5.id5)are provided with the same set of paper abstracts and asked to assess the extent to which each profile aligns with the actual competencies evidenced in the papers\. This comparative evaluation focuses on the fidelity and relevance of the proposed domain and competencies relative to the source material\.

### 5\.2Integration of Large Language Models

The pipeline incorporates multiple[SOTA](https://arxiv.org/html/2607.28282#id26.26.id26)\-[LLMs](https://arxiv.org/html/2607.28282#id5.5.id5), selected based on their diverse capabilities and strong performance across a range of tasks \(see Appendix[A\.1](https://arxiv.org/html/2607.28282#A1.SS1)\)\. Access is provided through the free\-tier or low\-cost[Application Programming Interfaces](https://arxiv.org/html/2607.28282#id2.2.id2)offered by platforms such as GROQ111[https://groq\.com/](https://groq.com/), Accessed: 2025\-04\-10, OpenAI222[https://openai\.com/](https://openai.com/), Accessed: 2025\-04\-10, and Google AI333[https://ai\.google\.dev/](https://ai.google.dev/), Accessed: 2025\-04\-10, enabling broad experimentation and scalability\. Each[LLM](https://arxiv.org/html/2607.28282#id5.5.id5)delivers robust text generation and comparison capabilities, aligning with the demands of both competency profile generation and pairwise evaluation\. Although proprietary constraints \(e\.g\., details regarding quantization or other internal optimizations\) remain undisclosed, they do not hinder the effective application of these[LLMs](https://arxiv.org/html/2607.28282#id5.5.id5)within the pipeline\.

In the pipeline, the*llama\-3\.1\-70B*Llama \([2024a](https://arxiv.org/html/2607.28282#bib.bib95)\)[LLM](https://arxiv.org/html/2607.28282#id5.5.id5)generates competency profiles from research abstracts, employing a higher temperature setting and multiple completions \(six per abstract\) to enhance diversity and comprehensiveness of outputs\. Subsequently,[LLMs](https://arxiv.org/html/2607.28282#id5.5.id5)including*gemma2\-9b\-it*Gemma \([2024](https://arxiv.org/html/2607.28282#bib.bib93)\),*llama\-3\.1\-8b*Llama \([2024b](https://arxiv.org/html/2607.28282#bib.bib96)\),*gpt\-4o\-mini*OpenAI \([2024](https://arxiv.org/html/2607.28282#bib.bib85)\),*gemini\-2\.0\-flash*DeepMind \([2025](https://arxiv.org/html/2607.28282#bib.bib105)\), and*mixtral\-8x7b*AI \([2024](https://arxiv.org/html/2607.28282#bib.bib97)\)perform pairwise evaluations of these generated profiles according to the previously established evaluation pipeline\. This combined use of multiple models enhances robustness and reduces potential biases associated with relying on a single[LLM](https://arxiv.org/html/2607.28282#id5.5.id5)\.

### 5\.3Data Sources

The primary input data for competency profile generation is derived from research publications and their abstracts\. Abstracts are obtained from publicly accessible repositories such as theKITopen444[https://www\.bibliothek\.kit\.edu/kitopen\.php](https://www.bibliothek.kit.edu/kitopen.php), Accessed: 2025\-04\-10andOpenAlex555[https://openalex\.org/](https://openalex.org/), Accessed: 2025\-04\-10\. To preserve the integrity of the data, minimal preprocessing is performed; the raw abstracts are passed directly to the[LLMs](https://arxiv.org/html/2607.28282#id5.5.id5), ensuring authenticity and consistency in evaluation\.

### 5\.4Implementation Challenges and Solutions

While the implementation was largely straightforward due to the availability of established tools and[APIs](https://arxiv.org/html/2607.28282#id2.2.id2), certain challenges were encountered:

##### Scalability

Handling the large number of[API](https://arxiv.org/html/2607.28282#id2.2.id2)requests required for pairwise evaluations across multiple[LLMs](https://arxiv.org/html/2607.28282#id5.5.id5)posed a potential bottleneck\. This was addressed by implementing efficient request handling and parallelization, ensuring that evaluations could scale with the size of the dataset\.

##### Contextual Consistency

The[LLM](https://arxiv.org/html/2607.28282#id5.5.id5)consistency initially exhibited significant inconsistency; Applying[SOTA](https://arxiv.org/html/2607.28282#id26.26.id26)prompting techniques, including[RAG](https://arxiv.org/html/2607.28282#id18.18.id18), chain\-of\-thought reasoning, and structured outputs, substantially improved inter\-model and intra\-model consistency across repeated evaluations, without any manual correction or human\-in\-the\-loop tuning\.

## 6Evaluation

To evaluate the proposed method for automated evaluation using[LLMs](https://arxiv.org/html/2607.28282#id5.5.id5), an experimental study was conducted\. This section outlines the evaluation strategy, introduces the dataset used, and presents the metrics and results related to[LLM](https://arxiv.org/html/2607.28282#id5.5.id5)quality\.

### 6\.1Evaluation Strategy

The evaluation strategy is based on an experimental setup that compares automated rankings generated by multiple[LLMs](https://arxiv.org/html/2607.28282#id5.5.id5)with expert judgments\. A total of 20 experts participated, each selecting 5–10 of their own publicly available research publications\. Experts initiated the process themselves by providing the abstracts of these publications, which ensured that any shared materials were already in the public domain\. Only abstracts were used in the experiments, thereby omitting personal identifiers such as author names or affiliations\. Although the content of the abstracts could theoretically allow an individual expert to be identified, no sensitive personal information was collected or processed in this study\.

The selected abstracts were processed by various[LLMs](https://arxiv.org/html/2607.28282#id5.5.id5)to generate competency profiles\. The resulting profiles were evaluated using two distinct ranking methods: \(1\)manual expert rankings, wherein participants assessed the quality and relevance of the generated profiles in relation to their actual expertise via a web interface, and \(2\)automated rankings, produced through an Elo rating pipeline that aggregated pairwise comparisons performed by the[LLMs](https://arxiv.org/html/2607.28282#id5.5.id5)\.

To assess the alignment between automated and expert\-generated rankings, correlation\-based metrics as described in Section[6\.2](https://arxiv.org/html/2607.28282#S6.SS2)were applied\. In addition, an ablation study using a single[LLM](https://arxiv.org/html/2607.28282#id5.5.id5)was conducted to explicitly illustrate the impact of combining multiple[LLMs](https://arxiv.org/html/2607.28282#id5.5.id5)\.

### 6\.2Evaluation Metrics

To quantify the degree of agreement between automated and expert\-generated rankings, the correlation metrics introduced in Section[2\.3](https://arxiv.org/html/2607.28282#S2.SS3)are applied: Spearman’s rank correlation coefficient \(Spearman’sρ\\rho\) and Kendall’s tau \(τ\\tau\)\. These metrics are particularly appropriate for ordinal ranking comparisons, effectively capturing both monotonic relationships and pairwise rank agreement without relying on assumptions of linearity or normality\.

### 6\.3Results and Analysis

We evaluate two primary strategies for integrating multiple[LLM](https://arxiv.org/html/2607.28282#id5.5.id5)evaluations into an Elo\-based ranking: Threshold\-Based Consensus and No Threshold updates as described in Section[4\.3](https://arxiv.org/html/2607.28282#S4.SS3)\. We first present results from a multi\-[LLM](https://arxiv.org/html/2607.28282#id5.5.id5)setup that pools judgments across all available[LLMs](https://arxiv.org/html/2607.28282#id5.5.id5), followed by a single\-[LLM](https://arxiv.org/html/2607.28282#id5.5.id5)analysis usingllama\-3\.1\-8b\. Spearman’sρ\\rhoand Kendall’sτ\\taucorrelations with expert rankings are reported alongside standard deviations and p\-values\.

Table 1:Correlation between Elo\-based and expert rankings with all[LLMs](https://arxiv.org/html/2607.28282#id5.5.id5)\. "No T\." indicates the No Threshold approach where every[LLM](https://arxiv.org/html/2607.28282#id5.5.id5)’s judgment triggers an update\.Table 2:Correlation between Elo\-based and expert rankings using onlyllama\-3\.1\-8b\. “No T\.” is the No Threshold approach\.#### 6\.3\.1Multi\-Model Results

Table[1](https://arxiv.org/html/2607.28282#S6.T1)shows that very high thresholds \(1\.0, 0\.9\) yield moderate correlations but suffer from a high draw rate, since even minimal disagreement nullifies a comparison\. Lowering the threshold to 0\.75 captures more partial agreements and improves performance substantially\. A simple majority requirement \(0\.5\) provides the best average correlations, and using No Threshold \(“No T\.” in the table\) is similarly effective\. Notably, the modest difference between 0\.5 and No Threshold suggests that Elo readily absorbs and balances minor disagreements when multiple[LLMs](https://arxiv.org/html/2607.28282#id5.5.id5)are involved\.

#### 6\.3\.2Single\-Model Results

Table[2](https://arxiv.org/html/2607.28282#S6.T2)illustrates that a single[LLM](https://arxiv.org/html/2607.28282#id5.5.id5), herellama\-3\.1\-8b, does not benefit from cross\-[LLM](https://arxiv.org/html/2607.28282#id5.5.id5)disagreement in the same way\. While relaxing the threshold to 0\.5 again delivers the strongest correlations, the No Threshold approach drops in effectiveness: contradictory judgments cannot be offset by another[LLM](https://arxiv.org/html/2607.28282#id5.5.id5)’s consensus\. Consequently, No Threshold ranks below 0\.5 in this scenario, even though both outpace higher thresholds such as 0\.9 and 1\.0\.

#### 6\.3\.3Observations and Takeaways

Overall, requiring strong consensus \(e\.g\., 90% or 100%\) frequently introduces too many draws and discards partial\-but\-informative judgments, resulting in weaker correlations with expert rankings\.

Loosening the threshold to a simple majority \(0\.5\) allows more comparisons to produce decisive wins or losses, clearly boosting Elo performance\. In the multi\-[LLM](https://arxiv.org/html/2607.28282#id5.5.id5)case, even the fully inclusive No Threshold option works well, suggesting that diverse[LLMs](https://arxiv.org/html/2607.28282#id5.5.id5)collectively moderate each other’s noise\. However, in a single\-[LLM](https://arxiv.org/html/2607.28282#id5.5.id5)context, No Threshold tends to admit contradictory signals that are not corrected by other[LLMs](https://arxiv.org/html/2607.28282#id5.5.id5), which slightly reduces ranking accuracy compared to a 0\.5 threshold\. These findings indicate that draws should not be overused, and that leveraging every moderate agreement signal is beneficial—particularly when multiple[LLMs](https://arxiv.org/html/2607.28282#id5.5.id5)are available to balance noise\.

On average, adjacent ranks differed by107107Elo points when a consensus threshold \(1\.01\.0–0\.500\.50\) was used and by159159points under the No\-Threshold setting\.

Correlations with expert rankings remain stable—within±0\.03\{\\pm\}0\.03—when varying the threshold between0\.500\.50and0\.750\.75, indicating that final rankings are robust to this choice\.

## 7Conclusion

This paper presented a scalable and reliable automated evaluation framework utilizing multiple[LLMs](https://arxiv.org/html/2607.28282#id5.5.id5)in combination with an Elo rating system, significantly enhancing the efficiency and consistency of assessments of[LLM](https://arxiv.org/html/2607.28282#id5.5.id5)\-generated texts\. The conducted evaluation demonstrated a strong alignment between automated rankings and expert judgments, thereby validating the multi\-[LLM](https://arxiv.org/html/2607.28282#id5.5.id5)approach\. The versatility of the presented framework supports broad applicability across diverse domains requiring nuanced textual evaluation, substantially reducing dependency on extensive human intervention\. Further research is encouraged, particularly focusing on optimizing computational efficiency to fully leverage the framework’s potential at scale\.

## 8Limitations

A primary limitation of our approach stems from the substantial computational overhead associated with inference\-heavy pairwise comparisons\. Specifically, the Elo\-based evaluation requires𝒪​\(n2\)\\mathcal\{O\}\(n^\{2\}\)comparisons, each necessitating multiple LLM inferences, including bidirectional checks\. This quickly becomes computationally intensive and potentially costly when employing large commercial[LLMs](https://arxiv.org/html/2607.28282#id5.5.id5), even for moderately sized evaluation sets\.

To mitigate the computational complexity, future work could investigate comparison\-based sorting algorithms, aiming to reduce the required number of evaluations from𝒪​\(n2\)\\mathcal\{O\}\(n^\{2\}\)down to𝒪​\(n​log⁡n\)\\mathcal\{O\}\(n\\log n\)or even𝒪​\(n\)\\mathcal\{O\}\(n\)\. Preliminary attempts at such sorting methods have encountered challenges, including frequent draws and a lack of guaranteed transitivity in comparisons produced by[LLMs](https://arxiv.org/html/2607.28282#id5.5.id5)\. Nevertheless, Elo ratings currently provide a stable numeric metric, highlighting closely matched profiles and indicating areas of uncertainty effectively\.

Another practical challenge arises when comparing highly similar items\. When the differences between items are subtle, individual[LLM](https://arxiv.org/html/2607.28282#id5.5.id5)evaluations can yield divergent outcomes due to inherent model biases and the varying strengths and weaknesses across different models\. This variability complicates the task of reliably distinguishing between items of near\-equivalent quality and can reduce the clarity and interpretability of rank\-based evaluation methods\.

In such cases, the Elo rating provides valuable insight into the relative quality of items, even when absolute differences are minimal\. In several instances, items with only marginal quality distinctions received nearly identical Elo scores—an outcome that is informative in its own right\. Notably, Elo\-based rankings also help surface atypical items—either exceptionally strong or weak—when their scores deviate significantly from the rest, offering a robust signal for identifying outliers within a set of closely matched candidates\.

Finally, the scope and robustness of our study remain constrained by the current size and diversity of the expert pool\. Although the initial correlations observed between automated and expert rankings are promising, expanding the evaluation across a broader spectrum of academic disciplines and increasing the sample size through ongoing expert recruitment would significantly enhance the validity and generalizability of the presented results\.

## Acknowledgments

We acknowledge the use of[Artificial Intelligence](https://arxiv.org/html/2607.28282#id1.1.id1)\([AI](https://arxiv.org/html/2607.28282#id1.1.id1)\) tools in this work\. Specifically, ChatGPT \([https://chat\.openai\.com/](https://chat.openai.com/)\) was employed for language assistance \(i\.e\., paraphrasing, polishing, and proofreading\), and GitHub Copilot \([https://github\.com/features/copilot](https://github.com/features/copilot)\) provided programming support\. Additionally, generative[AI](https://arxiv.org/html/2607.28282#id1.1.id1)assisted in identifying relevant evaluation metrics during the literature search\. All authors remain fully responsible for the accuracy and originality of the methods, results, and conclusions presented in this paper\.

## ETHICAL IMPACT STATEMENT

##### Adherence to the ACL Code of Ethics\.

Our work aligns with the principles outlined in the ACL Code of Ethics \([https://www\.aclweb\.org/portal/content/acl\-code\-ethics](https://www.aclweb.org/portal/content/acl-code-ethics)\)\. We have taken measures to minimize unintended harm by carefully handling data and clearly communicating the limitations of our methodology\.

##### Bias Propagation and Fairness\.

Despite efforts to mitigate biases by using multiple[LLMs](https://arxiv.org/html/2607.28282#id5.5.id5)and bidirectional comparisons, there remains a risk that patterns inherent in the underlying training data could be reproduced or even amplified\. This may lead to systematic favoring or disadvantaging of specific types of content or writing styles\. Users of this framework should remain aware of these inherent limitations and, where feasible, introduce additional safeguards—such as randomization, diverse model choices, or sampling checks—to reduce bias effects\.

##### Over\-Reliance on Automated Judgments, Accountability, and Transparency\.

Our methodology relies heavily on automated pairwise comparisons carried out by[LLMs](https://arxiv.org/html/2607.28282#id5.5.id5)\. While this saves time and effort, it can overlook subtle contextual or domain\-specific nuances that human experts might detect\. Moreover, Elo ratings and numerical scores can create an illusion of objectivity and precision—potentially obscuring the subjective nature of[LLM](https://arxiv.org/html/2607.28282#id5.5.id5)judgments, especially for high\-stakes decisions\. To address these concerns, we recommend maintaining a "human\-in\-the\-loop" approach—where domain experts review and validate the automated scores to ensure they align with expert judgment\. This could involve periodic audits or random sampling of the[LLM](https://arxiv.org/html/2607.28282#id5.5.id5)outputs to ensure that the automated system is functioning as intended\.

##### Privacy and Data Handling\.

Because the system repeatedly passes textual data \(e\.g\., abstracts of research papers\) to external[LLM](https://arxiv.org/html/2607.28282#id5.5.id5)APIs, there is a risk of exposing sensitive or private information\. In this work, we used only openly available data\. Any private or proprietary data should be anonymized or stripped of identifying details prior to evaluation\. Researchers must secure informed consent where necessary and ensure compliance with local data\-protection regulations, as well as any terms of service imposed by LLM providers\.

##### Responsible NLP Research Checklist\.

Checklist item A:The authors have added a dedicated limitations section, where we outline the methodological boundaries and constraints of our approach \(see Section[8](https://arxiv.org/html/2607.28282#S8)\)\. In addition, we discuss potential risks and harms, including misuse and bias, in the Ethical Impact Statement \(see Section[ETHICAL IMPACT STATEMENT](https://arxiv.org/html/2607.28282#Sx2)\)\.

Checklist item B:An artifact in the form of code was created to demonstrate the proposed methodology and support reproducibility\. No new data was collected for this work\. The data used for evaluation purposes consisted of existing materials created by members of our institution and was used with appropriate permission\.

Checklist item C:Yes, we conducted computational experiments using external[LLM](https://arxiv.org/html/2607.28282#id5.5.id5)APIs, as described in the Implementation section \(see Section[5](https://arxiv.org/html/2607.28282#S5)\)\. The experimental setup, including evaluation procedures and conditions, is detailed in the Evaluation section \(see Section[6\.1](https://arxiv.org/html/2607.28282#S6.SS1)\)\.

Checklist item D:Yes, we involved human experts from our institution in the evaluation process\. The experimental design and privacy considerations are described in detail in the Evaluation section \(see Section[6\.1](https://arxiv.org/html/2607.28282#S6.SS1)\)\. All participants worked with their own publicly available abstracts, and no personal or sensitive data was collected\.

Checklist item E:Yes, we used AI assistants during this research\. ChatGPT was employed for language\-related support, and GitHub Copilot assisted with coding\. We describe the use of these tools in the Acknowledgments section \(see Section[Acknowledgments](https://arxiv.org/html/2607.28282#Sx1)\), while emphasizing that all authors retain full responsibility for the scientific content of the paper\.

## References

- M\. Abdin, S\. A\. Jacobs, A\. A\. Awan,et al\.\(2024\)Phi\-3 Technical Report: A Highly Capable Language Model Locally on Your Phone\.arXiv\.External Links:[Document](https://dx.doi.org/10.48550/arXiv.2404.14219)Cited by:[§2\.1](https://arxiv.org/html/2607.28282#S2.SS1.p1.1)\.
- M\. AI \(2024\)Mistralai/Mixtral\-8x7B\-Instruct\-v0\.1\.Note:Accessed: 2025\-04\-11External Links:[Link](https://huggingface.co/mistralai/Mixtral-8x7B-Instruct-v0.1)Cited by:[§5\.2](https://arxiv.org/html/2607.28282#S5.SS2.p2.1)\.
- U\. Anwar, A\. Saparov, J\. Rando,et al\.\(2024\)Foundational Challenges in Assuring Alignment and Safety of Large Language Models\.arXiv\.External Links:[Document](https://dx.doi.org/10.48550/arXiv.2404.09932),[Link](http://arxiv.org/abs/2404.09932)Cited by:[§1](https://arxiv.org/html/2607.28282#S1.p2.1)\.
- M\. Boubdir, E\. Kim, B\. Ermis,et al\.\(2023\)Elo Uncovered: Robustness and Best Practices in Language Model Evaluation\.arXiv\.External Links:[Document](https://dx.doi.org/10.48550/arXiv.2311.17295),[Link](http://arxiv.org/abs/2311.17295)Cited by:[§3](https://arxiv.org/html/2607.28282#S3.p5.1),[§4\.4](https://arxiv.org/html/2607.28282#S4.SS4.p1.1)\.
- T\. B\. Brown, B\. Mann, N\. Ryder,et al\.\(2020\)Language Models are Few\-Shot Learners\.arXiv\.External Links:[Document](https://dx.doi.org/10.48550/arXiv.2005.14165),[Link](http://arxiv.org/abs/2005.14165)Cited by:[§4\.1](https://arxiv.org/html/2607.28282#S4.SS1.p2.1)\.
- Y\. Chang, X\. Wang, J\. Wang,et al\.\(2024\)A Survey on Evaluation of Large Language Models\.ACM Trans\. Intell\. Syst\. Technol\.15\(3\),pp\. 39:1–39:45\.External Links:[Document](https://dx.doi.org/10.1145/3641289),[Link](https://dl.acm.org/doi/10.1145/3641289)Cited by:[§1](https://arxiv.org/html/2607.28282#S1.p1.1),[§1](https://arxiv.org/html/2607.28282#S1.p2.1),[§3](https://arxiv.org/html/2607.28282#S3.p2.1),[§3](https://arxiv.org/html/2607.28282#S3.p4.1)\.
- C\. Chiang and H\. Lee \(2023\)Can Large Language Models Be an Alternative to Human Evaluations?\.arXiv\.External Links:[Document](https://dx.doi.org/10.48550/arXiv.2305.01937),[Link](http://arxiv.org/abs/2305.01937)Cited by:[§1](https://arxiv.org/html/2607.28282#S1.p2.1)\.
- G\. DeepMind \(2025\)Gemini 2\.0 Flash\.\(en\)\.External Links:[Link](https://deepmind.google/technologies/gemini/flash/)Cited by:[§5\.2](https://arxiv.org/html/2607.28282#S5.SS2.p2.1)\.
- D\. Deutsch, T\. Bedrax\-Weiss, and D\. Roth \(2021\)Towards Question\-Answering as an Automatic Metric for Evaluating the Content Quality of a Summary\.Transactions of the Association for Computational Linguistics9,pp\. 774–789\.External Links:[Document](https://dx.doi.org/10.1162/tacl%5Fa%5F00397),[Link](https://doi.org/10.1162/tacl%5C%5Fa%5C%5F00397)Cited by:[§1](https://arxiv.org/html/2607.28282#S1.p1.1)\.
- A\. E\. Elo \(1986\)The rating of chessplayers, past and present\.2nd ed edition,Arco Pub\.,New York\(eng\)\.Cited by:[§2\.2](https://arxiv.org/html/2607.28282#S2.SS2.p1.1)\.
- J\. Fu, S\. Ng, Z\. Jiang,et al\.\(2023\)GPTScore: Evaluate as You Desire\.arXiv\.External Links:[Document](https://dx.doi.org/10.48550/arXiv.2302.04166),[Link](http://arxiv.org/abs/2302.04166)Cited by:[§3](https://arxiv.org/html/2607.28282#S3.p3.1)\.
- T\. Gemma \(2024\)Gemma\.Note:Accessed: 2025\-04\-11External Links:[Document](https://dx.doi.org/10.34740/KAGGLE/M/3301),[Link](https://www.kaggle.com/m/3301)Cited by:[§5\.2](https://arxiv.org/html/2607.28282#S5.SS2.p2.1)\.
- J\. Gu, X\. Jiang, Z\. Shi,et al\.\(2025\)A Survey on LLM\-as\-a\-Judge\.arXiv\.External Links:[Document](https://dx.doi.org/10.48550/arXiv.2411.15594),[Link](http://arxiv.org/abs/2411.15594)Cited by:[§4\.1](https://arxiv.org/html/2607.28282#S4.SS1.p1.1)\.
- M\. H\. Jarrahi, D\. Askay, A\. Eshraghi,et al\.\(2023\)Artificial intelligence and knowledge management: A partnership between human and AI\.Business Horizons66\(1\),pp\. 87–99\.External Links:[Document](https://dx.doi.org/10.1016/j.bushor.2022.03.002),[Link](https://www.sciencedirect.com/science/article/pii/S0007681322000222)Cited by:[§1](https://arxiv.org/html/2607.28282#S1.p1.1)\.
- A\. Q\. Jiang, A\. Sablayrolles, A\. Mensch,et al\.\(2023\)Mistral 7B\.arXiv\.External Links:[Document](https://dx.doi.org/10.48550/arXiv.2310.06825),[Link](http://arxiv.org/abs/2310.06825)Cited by:[§2\.1](https://arxiv.org/html/2607.28282#S2.SS1.p1.1)\.
- M\. G\. Kendall \(1938\)A New Measure of Rank Correlation\.Biometrika30\(1/2\),pp\. 81–93\.External Links:[Document](https://dx.doi.org/10.2307/2332226),[Link](https://www.jstor.org/stable/2332226)Cited by:[§2\.3](https://arxiv.org/html/2607.28282#S2.SS3.p1.7)\.
- P\. Lewis, E\. Perez, A\. Piktus,et al\.\(2021\)Retrieval\-Augmented Generation for Knowledge\-Intensive NLP Tasks\.arXiv\.External Links:[Document](https://dx.doi.org/10.48550/arXiv.2005.11401),[Link](http://arxiv.org/abs/2005.11401)Cited by:[§2\.1](https://arxiv.org/html/2607.28282#S2.SS1.p2.1)\.
- A\. Lewkowycz, A\. Andreassen, D\. Dohan,et al\.\(2022\)Solving Quantitative Reasoning Problems with Language Models\.arXiv\.External Links:[Document](https://dx.doi.org/10.48550/arXiv.2206.14858),[Link](http://arxiv.org/abs/2206.14858)Cited by:[§4\.2](https://arxiv.org/html/2607.28282#S4.SS2.p1.1)\.
- P\. Liang, R\. Bommasani, T\. Lee,et al\.\(2023\)Holistic Evaluation of Language Models\.arXiv\.External Links:[Document](https://dx.doi.org/10.48550/arXiv.2211.09110),[Link](http://arxiv.org/abs/2211.09110)Cited by:[§1](https://arxiv.org/html/2607.28282#S1.p1.1),[§3](https://arxiv.org/html/2607.28282#S3.p4.1)\.
- C\. Lin \(2004\)ROUGE: A Package for Automatic Evaluation of Summaries\.InText Summarization Branches Out,Barcelona, Spain,pp\. 74–81\.External Links:[Link](https://aclanthology.org/W04-1013/)Cited by:[§3](https://arxiv.org/html/2607.28282#S3.p2.1)\.
- Y\. Liu, H\. Zhou, Z\. Guo,et al\.\(2025\)Aligning with Human Judgement: The Role of Pairwise Preference in Large Language Model Evaluators\.arXiv\.External Links:[Document](https://dx.doi.org/10.48550/arXiv.2403.16950),[Link](http://arxiv.org/abs/2403.16950)Cited by:[§4\.1](https://arxiv.org/html/2607.28282#S4.SS1.p1.1)\.
- M\. Llama \(2024a\)Meta\-llama/Llama\-3\.1\-70B\-Instruct\.Note:Accessed: 2025\-04\-11External Links:[Link](https://huggingface.co/meta-llama/Llama-3.1-70B-Instruct)Cited by:[§5\.2](https://arxiv.org/html/2607.28282#S5.SS2.p2.1)\.
- M\. Llama \(2024b\)Meta\-llama/Llama\-3\.1\-8B\.Note:Accessed: 2025\-04\-11External Links:[Link](https://huggingface.co/meta-llama/Llama-3.1-8B)Cited by:[§5\.2](https://arxiv.org/html/2607.28282#S5.SS2.p2.1)\.
- A\. Martino, M\. Iannelli, and C\. Truong \(2023\)Knowledge Injection to Counter Large Language Model \(LLM\) Hallucination\.InThe Semantic Web: ESWC 2023 Satellite Events,C\. Pesquita, H\. Skaf\-Molli, V\. Efthymiou, S\. Kirrane, A\. Ngonga, D\. Collarana, R\. Cerqueira, M\. Alam, C\. Trojahn, and S\. Hertling \(Eds\.\),Cham,pp\. 182–185\(en\)\.External Links:[Document](https://dx.doi.org/10.1007/978-3-031-43458-7%5F34)Cited by:[§2\.1](https://arxiv.org/html/2607.28282#S2.SS1.p2.1)\.
- MetaAI \(2024\)Llama 3\.2: Revolutionizing edge AI and vision with open, customizable models\.\(en\)\.External Links:[Link](https://ai.meta.com/blog/llama-3-2-connect-2024-vision-edge-mobile-devices/)Cited by:[§2\.1](https://arxiv.org/html/2607.28282#S2.SS1.p1.1)\.
- OpenAI, J\. Achiam, S\. Adler,et al\.\(2024\)GPT\-4 Technical Report\.arXiv\.External Links:[Document](https://dx.doi.org/10.48550/arXiv.2303.08774),[Link](http://arxiv.org/abs/2303.08774)Cited by:[§2\.1](https://arxiv.org/html/2607.28282#S2.SS1.p1.1)\.
- OpenAI \(2024\)GPT\-4o mini: advancing cost\-efficient intelligence\.\(en\-US\)\.Note:Accessed: 2025\-04\-11External Links:[Link](https://openai.com/index/gpt-4o-mini-advancing-cost-efficient-intelligence/)Cited by:[§5\.2](https://arxiv.org/html/2607.28282#S5.SS2.p2.1)\.
- K\. Papineni, S\. Roukos, T\. Ward,et al\.\(2002\)Bleu: a Method for Automatic Evaluation of Machine Translation\.InProceedings of the 40th Annual Meeting of the Association for Computational Linguistics,P\. Isabelle, E\. Charniak, and D\. Lin \(Eds\.\),Philadelphia, Pennsylvania, USA,pp\. 311–318\.External Links:[Document](https://dx.doi.org/10.3115/1073083.1073135),[Link](https://aclanthology.org/P02-1040/)Cited by:[§3](https://arxiv.org/html/2607.28282#S3.p2.1)\.
- X\. Pu, M\. Gao, and X\. Wan \(2023\)Summarization is \(Almost\) Dead\.arXiv\.External Links:[Document](https://dx.doi.org/10.48550/arXiv.2309.09558),[Link](http://arxiv.org/abs/2309.09558)Cited by:[§1](https://arxiv.org/html/2607.28282#S1.p1.1)\.
- M\. Schäfer, S\. Nadi, A\. Eghbali,et al\.\(2024\)An Empirical Evaluation of Using Large Language Models for Automated Unit Test Generation\.IEEE Transactions on Software Engineering50\(1\),pp\. 85–105\.External Links:[Document](https://dx.doi.org/10.1109/TSE.2023.3334955),[Link](https://ieeexplore.ieee.org/document/10329992)Cited by:[§1](https://arxiv.org/html/2607.28282#S1.p1.1)\.
- C\. Spearman \(2010\)The proof and measurement of association between two things\.International Journal of Epidemiology39\(5\),pp\. 1137–1150\(en\)\.External Links:[Document](https://dx.doi.org/10.1093/ije/dyq191),[Link](https://academic.oup.com/ije/article-lookup/doi/10.1093/ije/dyq191)Cited by:[§2\.3](https://arxiv.org/html/2607.28282#S2.SS3.p1.7)\.
- A\. Vaswani, N\. Shazeer, N\. Parmar,et al\.\(2023\)Attention Is All You Need\.arXiv\.External Links:[Document](https://dx.doi.org/10.48550/arXiv.1706.03762),[Link](http://arxiv.org/abs/1706.03762)Cited by:[§2\.1](https://arxiv.org/html/2607.28282#S2.SS1.p1.1)\.
- R\. Wang, F\. Mi, Y\. Chen,et al\.\(2024\)Role Prompting Guided Domain Adaptation with General Capability Preserve for Large Language Models\.arXiv\(en\)\.External Links:[Link](http://arxiv.org/abs/2403.02756)Cited by:[§2\.1](https://arxiv.org/html/2607.28282#S2.SS1.p2.1)\.
- J\. Wei, X\. Wang, D\. Schuurmans,et al\.\(2023\)Chain\-of\-Thought Prompting Elicits Reasoning in Large Language Models\.arXiv\.External Links:[Document](https://dx.doi.org/10.48550/arXiv.2201.11903),[Link](http://arxiv.org/abs/2201.11903)Cited by:[§2\.1](https://arxiv.org/html/2607.28282#S2.SS1.p2.1)\.
- N\. Wu, M\. Gong, L\. Shou,et al\.\(2023\)Large Language Models are Diverse Role\-Players for Summarization Evaluation\.InNatural Language Processing and Chinese Computing,F\. Liu, N\. Duan, Q\. Xu, and Y\. Hong \(Eds\.\),Cham,pp\. 695–707\(en\)\.External Links:[Document](https://dx.doi.org/10.1007/978-3-031-44693-1%5F54)Cited by:[§1](https://arxiv.org/html/2607.28282#S1.p1.1)\.
- T\. Zhang, V\. Kishore, F\. Wu,et al\.\(2020\)BERTScore: Evaluating Text Generation with BERT\.arXiv\.External Links:[Document](https://dx.doi.org/10.48550/arXiv.1904.09675),[Link](http://arxiv.org/abs/1904.09675)Cited by:[§3](https://arxiv.org/html/2607.28282#S3.p2.1)\.
- T\. Z\. Zhao, E\. Wallace, S\. Feng,et al\.\(2021\)Calibrate Before Use: Improving Few\-Shot Performance of Language Models\.arXiv\.External Links:[Document](https://dx.doi.org/10.48550/arXiv.2102.09690),[Link](http://arxiv.org/abs/2102.09690)Cited by:[§4\.2](https://arxiv.org/html/2607.28282#S4.SS2.p1.1)\.
- W\. X\. Zhao, K\. Zhou, J\. Li,et al\.\(2025\)A Survey of Large Language Models\.arXiv\.External Links:[Document](https://dx.doi.org/10.48550/arXiv.2303.18223),[Link](http://arxiv.org/abs/2303.18223)Cited by:[§4\.2](https://arxiv.org/html/2607.28282#S4.SS2.p1.1)\.
- L\. Zheng, W\. Chiang, Y\. Sheng,et al\.\(2023\)Judging LLM\-as\-a\-Judge with MT\-Bench and Chatbot Arena\.arXiv\.External Links:[Document](https://dx.doi.org/10.48550/arXiv.2306.05685),[Link](http://arxiv.org/abs/2306.05685)Cited by:[§3](https://arxiv.org/html/2607.28282#S3.p4.1)\.

## Appendix AAppendix

### A\.1Large Language Models Used

The following LLMs were integrated into the evaluation pipeline:

Table 3:LLMs used in the evaluation pipeline\. Profile generation uses temperature 0\.5 with 6 completions; evaluation uses temperature 0,1\. Quantization details are proprietary and undisclosed\.
### A\.2Example Competency Profiles

The following two profiles outline the competencies of two experts in the field of information extraction and community development\.

Demonstrative Profile 1Domain Expertise:Advancing information extraction through generative Large Language Models \(LLMs\)Competencies:•Information Extraction Technologies:Utilizes generative LLMs for structural text analysis, identifying entities, relations, and events\.•Cross\-Domain Adaptability:Applies LLMs across diverse domains, showcasing flexibility in understanding and generating domain\-specific texts\.•Systematic Literature Analysis:Conducts in\-depth reviews of contemporary research on LLM\-based information extraction techniques\.•Subtask\-Based Taxonomy:Categorizes advancements in LLM\-driven information extraction by subtasks and underlying learning paradigms\.•Trend Forecasting:Identifies emerging trends and anticipates future directions in LLM applications for information extraction\.•Community Contribution:Curates and regularly updates a public repository of relevant research on LLM\-enhanced information extraction\.

Demonstrative Profile 2Domain Expertise:Facilitating the development of scientific web communities through detailed competence analysis\.Competencies:•Competence Identification:Extracts and delineates individual competences with precision based on scientific publication data\.•Community Building:Supports the formation and growth of research communities by aligning and harmonizing diverse areas of expertise\.•Decision Support Systems:Integrates structured competence data into advanced decision\-making frameworks to enhance strategic outcomes\.•Team Formation:Enables effective team assembly through accurate competence mapping and role alignment\.•Knowledge Visualization:Employs sophisticated visualization tools to depict the development and interaction dynamics of virtual research communities\.•Expertise Analysis:Analyzes published research to identify optimal collaborations and recommend role assignments\.

### A\.3Example Prompt

The following listing presents the complete prompt structure used in our evaluation framework\. The prompt demonstrates a multi\-turn conversation between the system, user, and assistant, showcasing both an example evaluation and the actual task to be performed\.

Listing 1:Complete Example Prompt for Competency Profile Evaluation1System:Youareaskilledevaluatortaskedwithevaluatingtherelevanceoftwocompetencyprofilesthatwereextractedbyanothersystemfromprovidedscientificabstracts\.Eachprofileisexpectedtoreflectaspecificdomainofexpertiseandlist3toatmost8keycompetenciesdemonstratedbytheauthor\.Yourtaskistoevaluatehowwelleachprofilereflectsthecompetencies,themes,andexpertiseareasmentionedintheabstracts\.Comparethetwoprofilesanddeterminewhichoneismorerelevanttotheabstracts,structuringyourresponseasaJSONobjectasfollows:

2\{

3"reasoning":"\[YourEvaluationandReasoning\]",

4"preferred\_profile":\[1or2\]

5\}

6Youranalysisshouldbeneutral,accurate,anddetailed,basedonthecontentoftheabstractsprovided\.

7

8User:Example1:

9

10Abstract1:

11Patientslivinginunderservedareasdoregularlyexpressaninterestinstoneprevention;however,factorslimitingparticipation,asidefromobviouscostconsiderations,arelargelyunknown\.Tobetterunderstandfactorsassociatedwithcompliancewithsubmitting24\-hoururinecollections,wereviewedourpatientexperienceatthekidneystoneclinicatahospitalthatprovidescareforanunderservedurbancommunity\.Aretrospectivechartreviewofpatientstreatedforkidneyand/orureteralstonesbetweenAugust2014andMay2016wasperformed\.Patientdemographics,medicalcharacteristics,stonefactors,andcompliancedatawerecompiledintoourdataset\.Patientsweredividedintotwogroups:thosewhodidanddidnotsubmittherequestedinitial24\-hoururinecollection\.Analysisoffactorsrelatedtocompliancewasperformedusingunivariateanalysisandmultivariatelogisticregression\.Atotalof193patientsmetinclusioncriteriaforourstudy,42\.5%\(82/193\)ofwhomsubmitted24\-hoururinesamples\.Ofthe82collectionssubmitted,34\.1%\(28/82\)wereconsideredinadequatebycreatininelevel\.Asecondurinecollectionwithin6monthswasobtainedin14\.0%\(27/193\)ofpatients\.UnivariateanalysisdemonstratedthatAfricanAmerican\(AA\)patientswerelesslikelytosubmitaninitial24\-hoururinecollectionthanCaucasianpatients\(collected:30\.9%vs51\.8%;p<0\.05,respectively\)\.Patientswithafamilyhistoryofkidneystonesweremorelikelytosubmitaninitial24\-hoururinecollectionthanpatientswithoutafamilyhistoryofkidneystones\(61\.1%vs38\.2%,p<0\.02,respectively\)\.Onmultivariateanalysis,bothfactorsremainedsignificantpredictorsofcompliancewithsubmittinga24\-hoururinecollection\.Inourunderservedpatientpopulation,AApatientswerehalfaslikelytosubmita24\-hoururinecollectionthanCaucasianpatients,whereaspatientswithapositivefamilyhistoryofstonesweremorethantwiceaslikelytosubmitthanpatientswithnofamilyhistory\.

12

13Abstract2:

14Iatrogenicuretericinjuriesingynecologicsurgeryarequitecommon\.Thelaparoscopicspectraoftreatmentgivesawiderangeofapplication\.Wepresentthecaseofa40\-year\-oldfemalewhounderwenttotalabdominalhysterectomywithbilateralsalpingo\-oopherectomyfordysfunctionaluterinebleeding\.Postoperatively,shehadoliguriawithraisedcreatinineandwasfoundtohaveuretericnarrowingonmagneticresonanceurogram\.Thepatientwassuccessfullymanagedbylaparoscopic\(Boariflap\)ureteroneocystostomy\.

15

16Profile1:

17Domain:"HealthcareandPatientCare"

18

19Competencies:

20\-PatientDemographicsAnalysis:Demonstratedthroughthereviewofpatientcharacteristics,includingfactorssuchasraceandfamilyhistory,tobetterunderstandfactorsassociatedwithcomplianceinsubmitting24\-hoururinecollections\.

21\-MedicalChartReview:Showcasedbyconductingaretrospectivechartreviewofpatientstreatedforkidneyand/orureteralstonestocompilerelevantdataforanalysis\.

22\-StatisticalAnalysis:Exhibitedthroughtheuseofunivariateanalysisandmultivariatelogisticregressiontoidentifyfactorsrelatedtocomplianceinsubmitting24\-hoururinecollections\.

23\-ClinicalResearch:Shownbyinvestigatingfactorsassociatedwithcomplianceinsubmitting24\-hoururinecollectionsinanunderservedpatientpopulation\.

24\-PatientCare:Displayedthroughtheidentificationoffactorsthatmaylimitpatientparticipationinstoneprevention,suchasraceandfamilyhistory\.

25\-SurgicalIntervention:Demonstratedbypresentingacaseofapatientsuccessfullymanagedbylaparoscopic\(Boariflap\)ureteroneocystostomyfollowingiatrogenicuretericinjuriesingynecologicsurgery\.

26\-DiagnosticImagingInterpretation:Showcasedthroughtheinterpretationofmagneticresonanceurogramtoidentifyuretericnarrowinginapatientfollowinggynecologicsurgery\.

27\-ClinicalPracticeImprovement:Exhibitedbyidentifyingactionableinsights,suchasthedifferenceincompliancebetweenAfricanAmericanandCaucasianpatients,toimprovepatientcareandoutcomesinunderservedpopulations\.

28

29Profile2:

30Domain:"UrologyandSurgicalCare"

31

32Competencies:

33\-PatientDataAnalysis:Demonstratedthroughtheretrospectivechartreviewandanalysisofpatientdemographics,medicalcharacteristics,andcompliancedatatounderstandfactorsassociatedwithpatientparticipationinmedicalstudies\.

34\-SurgicalProcedureExpertise:Showcasedthroughthesuccessfulmanagementofapatientwithiatrogenicuretericinjuryusinglaparoscopicureteroneocystostomy,specificallytheBoariflaptechnique\.

35\-ResearchStudyDesign:Exhibitedbydesigningandconductingastudytoinvestigatefactorsrelatedtopatientcompliancewithsubmitting24\-hoururinecollections,includingdatacollectionandstatisticalanalysis\.

36\-StatisticalAnalysis:Demonstratedthroughtheuseofunivariateanalysisandmultivariatelogisticregressiontoidentifysignificantpredictorsofpatientcompliance\.

37\-CulturalCompetence:Shownbyrecognizingandaddressingdisparitiesinpatientcomplianceamongdifferentdemographicgroups,suchasAfricanAmericanpatientsbeinglesslikelytosubmit24\-hoururinecollections\.

38\-MedicalKnowledge:Exhibitedbyunderstandingtheclinicalsignificanceofcreatininelevels,familyhistoryofkidneystones,andothermedicalfactorsthatinfluencepatientcomplianceandtreatmentoutcomes\.

39\-CaseManagement:Demonstratedbyeffectivelymanagingapatient’spostoperativecomplications,includingoliguriaandraisedcreatinine,andprovidingappropriatetreatmentandfollow\-upcare\.

40\-ClinicalProblem\-Solving:Showcasedbyidentifyingandaddressingcomplexmedicalissues,suchasiatrogenicuretericinjuries,anddevelopingeffectivetreatmentplanstoimprovepatientoutcomes\.

41

42Assistant:\{

43"reasoning":"Bothcompetencyprofilesdemonstraterelevancetotheprovidedscientificabstracts,focusingonhealthcare,patientcare,andurology\.However,Profile1ismorecomprehensiveincoveringthebreadthofskillsandcompetenciesreflectedintheabstracts,includingpatientdemographicsanalysis,medicalchartreview,statisticalanalysis,clinicalresearch,patientcare,surgicalintervention,diagnosticimaginginterpretation,andclinicalpracticeimprovement\.Profile1explicitlymentionstheuseofstatisticalmethods\(univariateanalysisandmultivariatelogisticregression\)andhighlightstheimportanceofunderstandingpatientdemographicsandclinicalcharacteristicsinimprovingpatientcareandoutcomes\.Additionally,itcoversboththeclinicalandresearchaspectspresentedintheabstracts,suchasthemanagementofpatientswithkidneystonesandtheinvestigationoffactorsinfluencingcompliancewithmedicalprocedures\.Profile2,whilealsorelevant,seemstoemphasizemorethesurgicalandresearchdesignaspects,withoutfullyencapsulatingtherangeofcompetenciesdemonstratedintheabstracts,suchasthedetailedanalysisofpatientdemographicsandtheapplicationofdiagnosticimaging\.Therefore,Profile1ismorealignedwiththecompetenciesandthemespresentedintheabstracts\.",

44"preferred\_profile":1

45\}

46

47User:Pleaseassessthefollowingcompetencyprofileintermsofitsrelevancetothesescientificabstracts\.

48

49Abstracts:

50Abstract1:

51InComputerScience,SoftwareEngineering,BusinessInformaticsorInformationSystems,conceptualmodelingisanimportanttoolandassuchalsocontainedintherespectivecurricularrecommendations\.Especiallyinlargeuniversitycourses,anautomatedassessmentofmodelscanimprovethequalityofteachingandlearning\.Whiletherearemanydifferentapproachestoautomaticallyassessconceptualmodels,theseapproaches,however,oftenonlytackleasingleaspectorasingletypeofconceptualmodel\.Inthispaper,weaimtotakeacomprehensiveperspectiveonthetopicandshedlightonthecurrentstateoftheartandtechnique\.Furthermore,asassessmentapproacheshavetobedevelopedinaccordancewithappropriateteachingorlearningactivitiesanddesiredlearningoutcomes,weinquireinwhichsettingsautomatedassessmentapproachesareincludedandtowhichextentdidacticaspectsaretakenintoaccount\.Tothisend,wehaveconductedasystematicliteraturereviewinwhichweidentified110relevantpublicationsonthetopicwhichwehaveanalyzedinastructuredway\.Theresultsprovideanswerstofiverelevantresearchquestionsandpinpointopenissueswhichshouldbeinquiredinfurtherresearch\.

52

53Abstract2:

54InvielenAnwendungsbereichenderInformatikspieltdiegrafischeModellierungeinewichtigeRolle\.GrafischeModellekommenbeispielsweisebeiderGesch\\"aftsprozessmodellierungoderimRahmenderSoftwareentwicklungzumEinsatz,umkomplexeSachverhalte\\"ubersichtlichdarzustellen\.InderHochschullehrekommtderzeiteinekompetenzorientierteAusrichtungentsprechenderLehrveranstaltungenzukurz,ebensosinddieM\\"oglichkeitenzurtechnischenUnterst\\"utzungeingeschr\\"ankt\.DieindieserArbeitbehandeltenForschungsfragensinddahereinerkompetenzorientiertenAusrichtungdesPr\\"ufensaufdemGebietdergrafischenModellierungsowiederEntwicklungeinerentsprechendenE\-Assessment\-Plattformgewidmet\.ImRahmenderArbeitwurdeanhandtheoriebasierterundempirischerAns\\"atzeeinumfassendesKompetenzmodellentwickelt,dasLernzielef\\"urzentraleHandlungsbereichedergrafischenModellierungund\\"uberfachlicheKompetenzenbeschreibt\.EswurdeeinAufgabenkatalogerstellt,derAufgabentypenmitdenimKompetenzmodelldefiniertenLernzielenverkn\\"upft\.Erg\\"anzendwurdenexemplarischeBewertungsschemataundEmpfehlungenf\\"urdieGestaltunglernf\\"orderlicherFeedback\-NachrichtenaufBasisdesKompetenzmodellsabgeleitet\.DieErgebnisseunterst\\"utzenLehrendebeiderAuswahlvonLernzielenundderGestaltungkompetenzorientierterPr\\"ufungenanhandpassenderModellierungsaufgaben\.ZurUmsetzungkompetenzorientierterPr\\"ufungenaufdemGebietdergrafischenModellierungwurdeeineE\-Assessment\-Plattformentwickelt\.Dieseber\\"ucksichtigtverschiedenegrafischeModellierungssprachen,individuelleBewertungsschemataundFeedback\-Empfehlungen\.Zus\\"atzlichwurdenDienstezurautomatisiertenBewertungvonPetri\-Netzenerstellt,dieLernzielezusyntaktischen,semantischenundpragmatischenQualit\\"atsaspektenadressieren\.DieEinsatzf\\"ahigkeitderPlattformwurdeimpraktischenEinsatzinLehrveranstaltungenundPr\\"ufungendemonstriert\.Erg\\"anzendwurdenBefragungenzurBenutzungsfreundlichkeitundweiterenAspektendurchgef\\"uhrtunddieErgebnissederAnwendungderBewertungsdiensteaufeinerumfangreichenDatenbasisstudentischerPetri\-Netzeevaluiert\.

55

56Abstract3:

57Usinge\-learningande\-assessmentenvironmentsinhighereducationbearsconsiderablepotentialforbothstudentsandteachers\.Inthiscontributionwepresentanarchitectureforacomprehensivee\-assessmentplatformforthemodelingdomain\.Theplatform\-\-currentlydevelopedintheKEA\-Modproject\-\-featuresamicro\-servicearchitectureandisbasedondifferentinter\-operablecomponents\.Basedonthisidea,theKEA\-Modplatformwillprovidee\-assessmentcapabilitiesforvariousgraph\-basedmodelinglanguagessuchasUnifiedModelingLanguage\(UML\),EntityRelationshipdiagrams\(ERD\),PetriNets,Event\-drivenProcessChains\(EPC\)andtheBusinessProcessModelandNotation\(BPMN\)andtheirrespectivediagramtypes\.

58

59Abstract4:

60InvielenBereichenderWirtschaftsinformatikspieltdieErstellungkonzeptuellerModelleunterVerwendunggrafischerModellierungsspracheneinewichtigeRolle\.EntsprechendwichtigisteinefundierteGrundausbildung,diesichandenben\\"otigtenModellierungskompetenzenorientiertunddahernebentheoretischenauchpraktischeAspektederkonzeptuellenModellierungindenBlicknimmt\.DervorliegendeBeitragstelltersteErgebnisseausdemKEA\-Mod\-Projektvor,dassichmitderErstellungeines"digitalenFachkonzepts"imBereichdergrafischen,konzeptuellenModellierungbefasst\.Kernst\\"uckdiesesFachkonzeptsistdieUnterst\\"utzungderGrundausbildungindergrafischen,konzeptuellenModellierungdurcheinekompetenzorientierteE\-Assessment\-PlattformmitautomatisiertenundindividuellenBewertungs\-undFeedbackm\\"oglichkeiten\.

61

62Abstract5:

63DieKEA\-Mod\-Plattformerm\\"oglichtes,ModellierungsaufgabenmitverschiedenenModellierungssprachenwiez\.B\.UML,Petri\-Netzen,EPKoderBPMNdurchDozierendezuerstellenundvonStudierendenbearbeitenzulassen\.DiePlattformkamineinergro\\"senLehrveranstaltungmitca\.250StudierendenzumPiloteinsatz\.DieStudierendenkonntenmitHilfederPlattformunddesintegriertenModellierungswerkzeugseineAufgabenreihemitModellierungsaufgabenzuPetri\-Netzenbearbeitenundeinreichen\.Anschlie\\"senderhieltendieStudierendenautomatisiertgeneriertesFeedback\.DasPosterbeschreibtdieEvaluationdiesesPiloteinsatzesausderPerspektivederStudierendenundbietetersteErgebnisseinBezugaufdiePlattform\-UsabilityundzurwahrgenommenenLernf\\"orderlichkeitdesFeedbacks\.

64

65Profile1:

66Domain:"GraphicalModeling"

67

68Competencies:

69\-AutomatedAssessmentofConceptualModels:DemonstratedacrossAbstracts1,2,and5,thiscompetencyinvolvesthedevelopmentofautomatedassessmenttoolstoevaluateconceptualmodelsinvariouseducationalsettings\.

70\-UnderstandingofDifferentApproachestoConceptualModeling:FoundinAbstracts1and2,thiscompetencyinvolvesanunderstandingofvariousapproachestoconceptualmodeling,includingautomatedassessmenttechniques\.

71\-DevelopmentofaComprehensiveE\-AssessmentPlatform:DemonstratedacrossAbstracts3and4,thiscompetencyinvolvesthedevelopmentofacomprehensivee\-assessmentplatformforthemodelingdomain,incorporatingdifferentinter\-operablecomponents\.

72\-UnderstandingofDifferentGraph\-BasedModelingLanguages:FoundinAbstracts3and4,thiscompetencyinvolvesanunderstandingofdifferentgraph\-basedmodelinglanguages,includingUML,PetriNets,andBPMN\.

73\-DevelopmentofaMicro\-ServiceArchitecture:DemonstratedacrossAbstracts3and4,thiscompetencyinvolvesthedevelopmentofamicro\-servicearchitectureforthee\-assessmentplatform\.

74\-UnderstandingoftheImportanceofFeedbackinLearning:FoundinAbstracts3and4,thiscompetencyinvolvesanunderstandingoftheimportanceoffeedbackinlearning,particularlyinthecontextofgraphicalmodeling\.

75\-EvaluationoftheEffectivenessofAutomatedAssessmentTools:DemonstratedacrossAbstracts1,2,3,and4,thiscompetencyinvolvestheevaluationoftheeffectivenessofautomatedassessmenttoolsinvariouseducationalsettings\.

76\-UnderstandingoftheRoleofGraphicalModelinginTeachingandLearning:FoundinAbstracts1and2,thiscompetencyinvolvesanunderstandingoftheroleofgraphicalmodelinginteachingandlearning,particularlyinthecontextofconceptualmodeling\.

77

78Profile2:

79Domain:"GraphicalModeling"

80

81Competencies:

82\-AutomatedAssessment:Hasexperiencewithautomatedtoolsforevaluatingmodels,butmaynotfullyunderstandthenuancesofdifferentassessmentapproaches\.

83\-ConceptualModeling:Familiarwithvariousmodelingtechniques,butlacksdepthinunderstandingthetheoreticalfoundationsanddifferencesbetweenapproaches\.

84\-E\-AssessmentPlatformDevelopment:Involvedinthedevelopmentofane\-assessmentplatform,butmaynothaveacomprehensiveunderstandingofitsarchitectureortheintegrationofdifferentcomponents\.

85\-Graph\-BasedModelingLanguages:Knowsaboutseveralgraph\-basedmodelinglanguages,includingUMLandPetriNets,butmaynotbeproficientintheirapplicationorthenuancesofeach\.

86\-Micro\-ServiceArchitecture:Awareoftheconceptofmicro\-servicearchitecture,butmaynotfullygraspitsimplementationorbenefitsinthecontextofthee\-assessmentplatform\.

87\-FeedbackinLearning:Recognizestheimportanceoffeedback,butmaynothaveadeepunderstandingofhowtoeffectivelyimplementfeedbackmechanismsingraphicalmodelingeducation\.

88\-EvaluationofAssessmentTools:Hassomeexperiencewithevaluatingtheeffectivenessofautomatedassessmenttools,butmaynothaveasystematicapproachtothisprocess\.

89\-RoleofGraphicalModeling:Understandsthatgraphicalmodelingisusedinteachingandlearning,butmaynotfullyappreciateitssignificanceorhowitcontributestostudentlearningoutcomes\.

90

91YourevaluationmustfollowthisJSONformat:

92\{

93"reasoning":"\[YourReasoningandEvaluationinthatorder\]",

94"preferred\_profile":\[1or2\]

95\}

96Bespecificanddetailedinyourreasoningandprovidethenumberofthepreferredprofile\.

97

98Assistant:\{

99"reasoning":"Profile1issignificantlymorerelevanttotheprovidedscientificabstractsthanProfile2\.Itcomprehensivelycapturesthecompetenciesdemonstratedintheabstracts,suchasthedevelopmentofautomatedassessmenttoolsforconceptualmodels,theunderstandingofvariousgraph\-basedmodelinglanguages,andthecreationofacomprehensivee\-assessmentplatform\.Profile1alsoemphasizestheimportanceoffeedbackinlearningandtheevaluationofautomatedassessmenttools,whicharecentralthemesintheabstracts\.Incontrast,Profile2presentsamoresuperficialunderstandingofthesecompetencies,indicatingfamiliaritywithoutdemonstratingdepthorcomprehensiveknowledge\.Forinstance,whileitmentionsautomatedassessmentande\-assessmentplatformdevelopment,itlacksthedetailedinsightsandsystematicapproachesthatProfile1provides\.Additionally,Profile1effectivelyconnectsthecompetenciestospecificaspectsoftheabstracts,suchasthesystematicliteraturereviewandthedevelopmentofacompetencemodel,whicharecrucialforunderstandingthecontextandsignificanceoftheresearch\.Therefore,Profile1isthepreferredchoiceasitalignsmorecloselywiththethemesandexpertiseareashighlightedintheabstracts\.",

100"preferred\_profile":1

101\}