Tag
提出 RIVER 框架，将 LLM 多智能体交互分解为原子主张，评估收敛、探索与保留三个信息动态维度，并在 40 个新闻生成案例上比较五种交互策略的信息流动机制。
This arXiv paper reformulates structured generation as an ambiguity-aware inference problem, replacing single-reference supervision with a multi-valid learning framework that represents candidate outputs as graphs and selects among them via graph-based reasoning over semantic alignment and relational consistency, yielding improved accuracy and robustness under ambiguity.
EviDial introduces a benchmark that separates label agreement from evidence-grounded assessment in depression dialogue evaluation, revealing that 82.8% of participant-symptom cells lack sufficient textual evidence and that 69.2–96.9% of seemingly correct LLM predictions are actually unsupported by the dialogue.
The paper proposes a two-stage framework for reflective prompt optimization that decouples candidate-pool construction from validation allocation, achieving up to 11.22 percentage point gains over the initial solver on five benchmarks using Qwen3.5-9B and Llama-3.1-8B-Instruct.
This paper proposes a multi-granularity interaction network for relational triple extraction that jointly leverages word-word, entity-relation, and head-tail interactions to better handle unseen entities, achieving F1 improvements of 3.1%/20.5% on the NYT-R and WebNLG-R datasets in the Unseen metric.
论文提出 ARCS（Ambiguity Resolution Corpus for SQL），首个包含真实数据库上自然歧义的 text-to-SQL 基准，并提出“结构化消歧”范式取代自由对话澄清。实验表明歧义下 text-to-SQL 仍具挑战：GPT-6 SOTA 仅 51% 端到端执行准确率，开源模型均不超过 27%。
This paper analyzes 127,833 real clinical queries sent by 6,342 healthcare providers to an institutional LLM assistant, finds that actual use (dominated by documentation and knowledge retrieval) diverges sharply from the task mix covered by 58 public clinical benchmarks, arguing that benchmark scores poorly predict real-world clinical AI performance.
This arXiv paper shows that RoBERTa-based machine-text detectors track statistical complexity rather than machine authorship: "humanizing" LLM outputs via Mistral-7B actually raises Verb Diversity and detection scores, while the detector exhibits a 76.3% false-positive rate on formal human writing and is evaded by 41% of homoglyph attacks. Event-based latent-space detection proved fragile to paraphrasing and character-level perturbations.
This paper empirically investigates the relationship between salience and answerability of questions in naturalistic dialogue, finding a robust but low positive correlation that is weaker than in monologic text, indicating that conversational structure is less predictable.
This paper introduces Coupled Usage–Sense Processes (CUSP), a hierarchical model for analyzing lexical semantic change by quantifying timing, mechanisms, and attributions in word usage over time.
This paper proposes a knowledge graph-based evaluation framework for assessing the contextual understanding of large language models in question answering, introducing a new similarity measure called S3KG that achieves superior performance over existing baselines.
This paper introduces ℓp-LoRA, a principled method for automatic rank allocation in low-rank adaptation using ℓp regularization, demonstrating competitive performance on NLP tasks.
The study introduces SHAP-RTL, a rendering layer that corrects the visualization of SHAP and LIME explanations for right-to-left languages, addressing issues like token sequence and script shaping while preserving original attribution values.
This paper replicates a distributional-semantics extractive summarization method for Hindi and evaluates it on standard corpora, finding that sentence position is the only contributing feature and current Hindi benchmarks fail to incentivize advanced content selection.
The article likely presents research on contrastive language models, exploring the use of contrastive learning techniques in language model development.
The paper proposes retrieval strategies to filter irrelevant HTML content for LLM-based autonomous web agents, improving performance on benchmarks like WebArena.
This paper introduces NAF-Bench to study how large language models adhere to specified negation semantics, finding that frontier models like o4-mini perform well while open-source models lag, and suggesting improvements via solver delegation or fine-tuning.
This paper introduces UGTPhon, a benchmark for grapheme-to-phoneme conversion in user-generated text, and presents a compositional approach that improves performance by leveraging canonical forms.
This paper presents design principles and the 'AGIMUD' software for enabling socio-affective interactions between humans and multiple AI agents in simulated dynamic worlds, leveraging generative AI and distributed processing.
This paper introduces Arafa, a large-scale Arabic fact-checking dataset generated using LLMs, aimed at addressing the scarcity of resources for automatic fact-checking in Arabic.