VERA-MH: Validation of Ethical and Responsible AI in Mental Health

# Validation of Ethical and Responsible AI in Mental Health
Source: [https://arxiv.org/html/2605.13318](https://arxiv.org/html/2605.13318)
Luca Belli Spring Health UC Berkeley Kate H\. Bentley Spring Health Josh Gieringer Spring Health Emily Van Ark Spring Health Nilu Zhao Spring Health Pradip Thachile Spring Health Matt Hawrilenko Spring Health Millard Brown Spring Health Adam M\. Chekroud Spring Health Yale University

###### Abstract

Chatbot usage has increased, including in fields for which they were never developed for—notably mental health support\. To that end, we introduce Validations of Ethical and Responsible AI in Mental Health \(VERA\-MH\), a novel clinically\-validated evaluation for safety of chatbots in the context of mental health support\. The first iteration of VERA\-MH focuses on Suicidal Ideation \(SI\) risks, by assessing how well chatbots can responds to users that might be in crisis\.

VERA\-MH is comprised of three steps: conversation simulation, conversation judging and model rating\. First, to simulate conversations with the chatbot under evaluation, another chatbot is tasked with role\-playing users based on specific personas\. Such user personas have been developed under clinical guidance, to make sure that, among others, multiple risk factors, demographic characteristics and disclosure factors were represented\. In the judging step, a second support model is used as an LLM\-as\-a\-Judge, together with a clinically\-developed rubric\. The rubric is structured as a flow, with a single Yes/No question asked each time, to improve answers’ consistency and highlight models’ failure modes\. In the last stage, results of each conversation are aggregated to present the final evaluation of the chatbot\. Together with the framework, we present the result of the evaluations for four leading LLM providers\.

## 1Introduction

The use of Large Language Model \(LLM\) based chatbots has expanded to virtually every field, changing how information is accessed and produced\. Chatbots’ great versatility allows them to be used in fields in which they have not been developed for, tested on, or for which there is insufficient regulation\. One such field is mental health, with chatbots transforming the way people access, seek support, and think it\[[27](https://arxiv.org/html/2605.13318#bib.bib41),[32](https://arxiv.org/html/2605.13318#bib.bib42),[10](https://arxiv.org/html/2605.13318#bib.bib43)\]\. In the U\.S\. alone, one in eight adolescents and young adults use AI chatbots for some form of mental health support\[[33](https://arxiv.org/html/2605.13318#bib.bib45)\]\.

It is estimated\[[53](https://arxiv.org/html/2605.13318#bib.bib59)\]that , in 2021, 746,000 people died from suicide occurred in 2021\. In the United States, the American foundation for Suicide prevention reports that in 2024, it was the 10th leading cause of death, with more than 48,000 people dying from suicide, and 2\.2 millions attempts\[[21](https://arxiv.org/html/2605.13318#bib.bib27)\]\. Recent tragedies\[[56](https://arxiv.org/html/2605.13318#bib.bib68),[45](https://arxiv.org/html/2605.13318#bib.bib67),[5](https://arxiv.org/html/2605.13318#bib.bib65),[13](https://arxiv.org/html/2605.13318#bib.bib66)\], have brought more attention to the role of chatbots in romanticizing suicidal thoughts or even actively providing information about suicide methods to facilitate suicide attempts, especially for vulnerable populations, such as youth\[[34](https://arxiv.org/html/2605.13318#bib.bib28)\]\. OpenAI, as one example, recently reported that 1\.2 million peopleper weekexpress suicide intent or plan during conversations with ChatGPT\[[36](https://arxiv.org/html/2605.13318#bib.bib46)\]\. There is an increasing need and urgency to develop evaluations to meaningfully test both the capabilities and safety of chatbots, especially important in highly consequential contexts such as mental health\.

We introduce a new evaluation to test LLM\-based chatbots for safety in a mental health context: the Validation of Ethical and Responsible AI in Mental Health \(VERA\-MH\), to bring more clinical expertise in the domain\. VERA\-MH is the product of a multi\-disciplinary effort, in which subject matter experts—AI developers, practicing clinicians, and suicide prevention experts—co\-designed the evaluation\. VERA\-MH not only is open\-source, but explicitly solicited feedback from the community, during a 60 days request for feedback period\. This paper is the result of the feedback received together and builds upon\[[8](https://arxiv.org/html/2605.13318#bib.bib2)\]\. We intentionally focus on a single high\-risk clinical issue, suicidal ideation \(SI\), rather than attempting to cover mental health safety broadly\. This focused scope enables deeper clinical specificity, clearer safety expectations, and more actionable assessment criteria, while providing a foundation for future expansion to additional mental health domains\.

Following the Hippocratic oath of “first, do no harm,” VERA\-MH is constructed to test thesafetyof chatbots, rather than evaluating theirefficacy\. VERA\-MH consists of three main parts\. First, a conversations simulator, in which synthetic conversations are created with the help of a supporting LLM tasked to role\-play as specific personas\. The simulated conversations are then evaluated against a clinically developed rubric reflecting current best practices for human\-chatbots interactions and evidence\-based suicide prevention strategies\. The judged conversations are then aggregated to provide a chatbot evaluation card\. After detailing the framework and its design principles, we report the results of evaluation of 4 of the main LLM providers\.

## 2Previous Work

Evaluation of LLMs and LLM\-based applications is a relatively new and dynamic field\. Even with the ever\-increasing number of evaluations and benchmarks published, standardized best practices and interoperability are still lacking\[[44](https://arxiv.org/html/2605.13318#bib.bib33),[54](https://arxiv.org/html/2605.13318#bib.bib34)\], with some efforts starting in that direction\[[19](https://arxiv.org/html/2605.13318#bib.bib29),[6](https://arxiv.org/html/2605.13318#bib.bib9)\]\. Evaluations’ critiques\[[42](https://arxiv.org/html/2605.13318#bib.bib31),[18](https://arxiv.org/html/2605.13318#bib.bib32)\]include the lack of construct validity\[[7](https://arxiv.org/html/2605.13318#bib.bib24)\], especially important in clinical use cases\[[2](https://arxiv.org/html/2605.13318#bib.bib25)\], lack of real\-world usefulness of the task\[[43](https://arxiv.org/html/2605.13318#bib.bib37)\], context collapse\[[26](https://arxiv.org/html/2605.13318#bib.bib44)\], lack of reliability\[[41](https://arxiv.org/html/2605.13318#bib.bib30)\], and the politics and incentives behind the evaluations\[[24](https://arxiv.org/html/2605.13318#bib.bib7),[47](https://arxiv.org/html/2605.13318#bib.bib22)\]\.

In a field like mental health, unfortunately chronically understaffed, LLMs benchmarks, based on synthetic conversations, have been created as a way to help train professionals while respecting patients’ privacy\[[30](https://arxiv.org/html/2605.13318#bib.bib10),[52](https://arxiv.org/html/2605.13318#bib.bib11),[39](https://arxiv.org/html/2605.13318#bib.bib12),[51](https://arxiv.org/html/2605.13318#bib.bib13)\]\. Regarding chatbots, researchers have found that insufficient guardrails\[[35](https://arxiv.org/html/2605.13318#bib.bib18)\]were present in deployed systems, which, given the sheer number of users of such systems, is deeply troubling and highlights the need for more effective\[[17](https://arxiv.org/html/2605.13318#bib.bib52)\]pre\-deployment safety evaluations\. Static\[[55](https://arxiv.org/html/2605.13318#bib.bib14)\], or a single\-turn dataset\[[4](https://arxiv.org/html/2605.13318#bib.bib15)\], while still helpful, are unable to capture the full context of a real conversation\. Users might disclose information regarding SI, possibly in passing or indirect form, after many turns\. Furthermore, if the same response, not unsafe in isolation, is given many times, the overall conversation could be harmful, or even unsafe, if risks are not sufficiently addressed\.

VERA\-MH is a safety, judge\-based, evaluation, with a conversation generation environment, to simulate a user dynamically interacting with a chatbot\. Unlike efficacy evaluations, such as\[[48](https://arxiv.org/html/2605.13318#bib.bib51),[35](https://arxiv.org/html/2605.13318#bib.bib18)\], the goal is not judging how chatbot responses might align with clinicians for diagnosis or treatment,, but only whether the responses of the chatbots are safe\. Like HealthBench\[[3](https://arxiv.org/html/2605.13318#bib.bib47)\], VERA\-MH is a judge\-based evaluation, aimed at replicating clinicians’ judgments\. However, VERA\-MH also contains a full conversation simulation engine, similar to Mindeval\[[40](https://arxiv.org/html/2605.13318#bib.bib17)\]\. Unlike it, however, VERA\-MH is specifically scoped down to a single safety issue, SI, for a more precise evaluation\.

## 3Design Principles

VERA\-MH was created with the following design principles in mind, which we believe to be necessary for the evaluation to be meaningful, scientifically\-grounded, and clinically valid\.

1. 1\.Clinically informed\. Practicing clinicians co\-designed the evaluation to guarantee clinical best practices are adequately reflected\.
2. 2\.Real\-world usage\. Chatbots should be evaluated on tasks reflective of real use cases, rather thanin silicoscenarios\.
3. 3\.Narrow scope\. For the evaluation to be meaningful, it should be tightly scoped, rather than being a catch\-all mental health evaluation\. This iteration of VERA\-MH focuses on SI risk\.
4. 4\.Conversation LevelThe evaluation focuses at the conversation level, since single\-turn evaluation can be too narrow in a mental health context\. This also implies tee valuation is: 1. \(a\)Multi\-turn\. To comprehensively reflect real\-world, complex interactions between a user and a chatbot, the evaluation focuses on multi\-turn evaluation\. 2. \(b\)Memoryless\. Each conversation is evaluated independently\.
5. 5\.Dynamic\. Conversations are dynamically generated, for each run of the evaluation\.
6. 6\.API\-based\. VERA\-MH is an API\-level evaluation\. Only the response of the model \(i\.e\., the text\) is evaluated, ignoring everything else, including elements present in the graphical user interface, such as pop\-ups or timers, or human escalation workflows that occur outside of the conversation\.
7. 7\.Automated\. To keep up with the pace of innovation, and the rapid development of models’ new capabilities and affordances, the evaluation is automated\. This allows for new models to be quickly evaluated before they are deployed\.
8. 8\.Validated by Experts\. Given the automated nature of the evaluation, it is important to verify that the results are consistent with experts \(in this case, practicing clinicians\)\.
9. 9\.Multi\-metric\. The complexities and novelty of the domain warrant a multi\-metric measure of performance for each model\.
10. 10\.Open\-sourceAll the code is open\-source, to guarantee transparency and repeatability\.
11. 11\.Accessible to non\-developers\. Given the multi\-stakeholder nature of the evaluation, the criteria defining safe vs\. unsafe behaviors \(i\.e\., detailed rubric content\) and the personas should be in a format accessible to everyone, rather than only existing in code\.
12. 12\.Constantly evolving\. The consensus on what constitutes best practice is evolving in the emerging field of mental health AI\. We acknowledge that each version of VERA\-MH reflects the state\-of\-the\-art at the time of its release and that the guidance might change—even dramatically—over time\.

## 4The Architecture

VERA\-MH is an evaluation pipeline consisting of three parts, thegeneration,judging, and the*rating*step\. Those can be run in sequence, or each part can be run independently\.

We introduced a first ancillary mode, an LLM tasked to role\-play as a user of the system, given our requirements of both multi\-turn and automated evaluations, discarding single prompts, scripted conversation, or human\-driven conversations\. Scripted conversations are not reflective of how a real conversation would flow given the contextual responses of chatbot\. Human conversation, in which people are tasked to pretend to be users of the systems, raise ethical questions, are not automated, are expensive and do not scale\. Conversations are evaluated against a rubric encoding best practices for chatbot\-human interactions regarding suicide risk and evidence\-based suicide prevention practices\. A second ancillary model, operating as a LLM\-as\-a\-Judge\[[58](https://arxiv.org/html/2605.13318#bib.bib1)\], guarantees fast and automated evaluation\. In Section[4\.2\.3](https://arxiv.org/html/2605.13318#S4.SS2.SSS3)we expand on the validity of such an approach and how it compares with expert human raters\. In the third and final step, the judged conversations are grouped to produce the final metrics of the evaluation\.

### 4\.1Conversation generation

To create conversations that are both automated and dynamic \(i\.e\., not scripted and thus changing with each evaluation\), we rely on another LLM tasked to role\-play as the user, interacting with the chatbot under evaluation\. A number ofpersonasare used to guide the LLMs in their role\-played users\. A system prompt instructs the user\-LLM to simulate users based on the specific personas as accurately as possible, and includes stylistic instructions such as matching the generated language and tone with the characteristics of the persona\. As shown in Figure[7](https://arxiv.org/html/2605.13318#A2.F7), users’ responses tend to be shorter in length, suggesting a correct interpretation of instructions\. Each conversation is simulated on a fresh LLM instance, making all conversations independent from each other\. In early versions, the user\-LLMs would produce grammatically correct, multi\-paragraphs responses, sometimes indicating states of mind in between asterisks, not representative of human\-chatbot interactions\. Similarly, the user\-LLMs would profusely thank the chatbot for their responses, and spend a lot of time in pleasantries before conversations\. To increase the realism, user\-LLMs were also instructed to cut the conversation off if they felt that they would not get any more good information from the chatbot, resulting in many conversations being closed by the simulated users over apparent frustration\. See Appendix[B](https://arxiv.org/html/2605.13318#A2)for statistics on the generated texts\.

#### 4\.1\.1Personas

To better control the simulated conversations, we created 100personas\. Each persona has unique characteristics on both demographic and clinical dimensions\. Demographics include age \(which has shown to influence the language used\), gender, and financial stress\. Important clinical markers are suicide risk level, history of suicidal thoughts, disclosure, access to mental health, social isolation, and recent triggers\. The full list can be found in the code repository\. To generate the personas the following procedure was followed\. Clinicians decided to have a distribution among the level of suicide risk of 30% for high, 30% for medium, 30% for low, and 10% for no\-risk to use as a control group\. For each of this group, other characteristics—including clinical, personal, and demographic ones—were assigned at random, and independently\. We used GPT5\.2 to create biography elements and one seed phrase suggestions\. Grok 4 created a second phrase suggestion\. Clinicians performed a final check to the resulting list to guarantee accuracy and representativeness\. This set of personas represents a 10x increase with respect to the 10 that were originally developed for\[[8](https://arxiv.org/html/2605.13318#bib.bib2)\]\. As highlighted in the Discussion session, there is a concrete risk for the LLM to stereotype the demographic of the personas, rather than perform accurate model users, a risk that is particularly evident at the intersection of multiple identities\[[14](https://arxiv.org/html/2605.13318#bib.bib55)\]\. This is why no “race” category is present, and why the names are not passed as part of the system prompt to avoid biasing\[[38](https://arxiv.org/html/2605.13318#bib.bib64)\]\.

### 4\.2Conversation Judging

Conversations are judged using a LLM\-as\-a\-Judge framework\[[58](https://arxiv.org/html/2605.13318#bib.bib1)\]with a clinically developed rubric\. This step can be run independently from the previous, if conversations are available, e\.g\., coming from production data\.

#### 4\.2\.1The Rubric

The rubric is where clinical expertise is concentrated\. It reflects current thinking on best practices for evaluating the safety of chatbots responding users at risk of suicide\. Likely the component of VERA\-MH that will continue to evolve the most in the future, as the field moves forward at the speed of technological change, and consensus about best practices evolves with it\.

The rubric contains five quasi\-independent dimensions:Detects Potential Risk,Confirms Risk,Guides to Human Care,Supportive Conversation,Follows AI Boundaries\. For each conversation, and for each dimension the Judge\-LLM is asked to pick one of the four options:Best Practice,Suboptimal but Low Potential for Harm,High Potential for Harm,Not Relevant\. An overview of the dimensions and their clinical meaning is presented in Table[1](https://arxiv.org/html/2605.13318#S4.T1)\. The rubric encodes asafetyevaluation, not aneffectivenessone\. The goal of the rubric, thus, is not to determine adherence to any treatment, or the overall effectiveness in treating a condition, but rather how well chatbots are able to pick on risk signals and respond appropriately\. However, determining whether responses to suicidal thoughts are appropriate inherently include first detecting if risk is present and confirming that such risk is present, including how immediate the risk is \(because immediacy informs what responses should come next\)\. TheDetects Potential Riskdimension should not be used as a standalone marker of risk detection accuracy, but rather as an indivisible part of the evaluation\.

Table 1:Rubric dimensions and their brief descriptions
#### 4\.2\.2LLM\-as\-a\-Judge

For each conversion, a fresh instance of the judge\-LLM receives a prompt with general instructions on the task, and the conversation as its system prompt\. Then, a flow of up to 35 multiple\-choice questions is passed to the Judge\-LLM, with the majority of them being binary \(Yes/No\), and a few ternary ones \(Yes/No/Not Relevant\)\. The answer to each question determines the next question\. Generally speaking, each question is asked in the form “does the chatbot response is harmful is this way?”, or “does the chatbot neglect to do this important behaviour?” Positive answers imply that the chatbot does not reflect current best practices for that dimension, skipping the rest of the questions in the same dimensions, if present, as we are only interested in the general rating, not the complete list of failure modes for each dimension\. The severity of the harm \(tracked in the rubric\) determines the rating, between “High potential for Harm” and “Suboptimal\.” The questions are presented in decreasing order of severity, and are tightly scoped to reduce variability\.

A negative answer prompts the next question of the dimension\. ABest Practicerating can be given only when all questions in the dimensions have been exhausted and no further harmful behaviour can be detected\. If no risk is present in the chat, dimensions are marked as non\-relevant and the work on the current conversation ends\. This is expected to happen, for example, in the control personas\.

We found that using this flow\-chart\-like, item\-level structure to operationalize the rubric increased rating consistency for both human clinicians with each other, and for human\-LLM comparisons\[[9](https://arxiv.org/html/2605.13318#bib.bib3)\]\. The added benefit of this approach is to make clear why a specific dimension did not receive the highest rating, by highlighting the specific questions \(and thus the corresponding not optimal behaviour\) was present\. In that respect, VERA\-MH can give concrete and actionable advice on how to improve the safety of chatbots\.

In the initial versions, the full rubric and the conversation were both passed as a system prompt to the LLM\-judge\. While the final 4 ratings were given as requested, why a specific rating was selected was opaque and impossible to really understand\. While it’s possible to have an “Explanation” field as part of the response, its accuracy and trustworthiness is debatable\.

#### 4\.2\.3Human Validation

While the usage of automated LLM\-based judges is necessary to guarantee automated and fast evaluations, it raises the question of \(criterion\) validity, i\.e\., how much the automated judges can be a replacement for human ones\. As reported by\[[9](https://arxiv.org/html/2605.13318#bib.bib3)\], calibrated expert humans \(practicing clinicians\) have an average achieve a chance\-corrected Inter\-Rater Reliability \(IRR\) of 0\.77 with one another when using the VERA\-MH rubric to rate the same simulated conversations for safety\. The evolution of the rubric, including the scoping down of questions and the usage of the flow structure, has helped achieving of at least 0\.77 between human experts and LLM judges when rating the same conversations\. The same conversation can be judged by different models, or by the same models multiple times to test for judge stability\. While we refer back again to\[[9](https://arxiv.org/html/2605.13318#bib.bib3)\]for the full analysis, LLM\-judge to LLM\-judge IRR is 0\.78\.Those results give us confidence that using LLM\-as\-a\-Judge is appropriate and outputs can generally be trusted in this context\.

### 4\.3Rating Models

After conversations are judged, the result of the evaluation is a matrix of \(dimensions×\\timesrating\), that is constructed as follows\. First non\-relevant conversations per each dimension \(defined as the conversions in which the LLM judge determined that no suicide risk was present\) are counted, and their percentage as a share of the total is added in the corresponding row\. Note that there is no guarantee that each dimension has the same percentage of non\-relevant conversation\. This happens when potential risk is detected, but the user denies any suicidal thoughts, making theGuides to Human Carenon\-relevant\. The remaining conversations \(i\.e\., the relevant ones\) are normalized to 1\. The\(i,j\)\(i,j\)\-th cells represents the percentage of relevant conversations that were scored for theii\-th criterion with thejj\-th rating\. While it’s true that the lack of normalization of rows is counterintuitive, and possibly confusing, it was a deliberate choice to prevent non\-relevant conversations to interfere with the rating\.

An example of the evaluation results for one of each of leading LLM providers can be found in Figure[1](https://arxiv.org/html/2605.13318#S5.F1), with more reported in Appendix[A](https://arxiv.org/html/2605.13318#A1)\.

The end to end pipeline to generate the rating of the models is as follows\. First, conversations are generated based on the above personas\. Our recommendation is to run 100 personas, 2 conversations per persona with a maximum of 30 turns, as an upcoming pre\-print focused on stability shows\.

## 5Experiments

We report the results of an experiment in which we use the recommended settings described in the above Section[4\.3](https://arxiv.org/html/2605.13318#S4.SS3)and the defaults are left untouched, with one exception\. For the GPT5\.X family of models, the parameter`max\_tokens`was set to50005000, as the default value didn’t produce results, because the token balance was used for internal reasoning\. The temperature for the LLM\-judges was set up to0, to reduce variation in their answers\.

Figure[1](https://arxiv.org/html/2605.13318#S5.F1)reports the result of the experiment for the flagship models in each family: Claude Opus 4\.7, GPT\-5\.4, Gemini 3 Pro Preview, Grok 4\.

![Refer to caption](https://arxiv.org/html/2605.13318v1/images/all.png)Figure 1:Results of the experiments\. For each dimension, the Non Relevant column is computed as a fraction of the total, then the remaining ones are normalized to one, which is why the row totals are more than 1\. This prevents the Non Relevant results to skew the results\.
## 6Addressing the Main Critiques in Current Evaluations Practices

The practice of AI evaluation is still evolving and has not yet reached maturity\. As noted in the literature\[[42](https://arxiv.org/html/2605.13318#bib.bib31),[18](https://arxiv.org/html/2605.13318#bib.bib32)\], current evaluation and benchmark practices have many pitfalls, including the lack of real world utility\[[43](https://arxiv.org/html/2605.13318#bib.bib37)\], inappropriate construct validity\[[7](https://arxiv.org/html/2605.13318#bib.bib24)\], and being motivated more by marketing and publicity than by scientific rigor and understanding\[[24](https://arxiv.org/html/2605.13318#bib.bib7)\]\. In this Section, we are going to discuss the ways in which VERA\-MH addresses such criticisms\.

### 6\.1Arbitrary selection

Curation is not a neutral process, quite the opposite\. Selecting what gets included in an evaluation, and thus measured, is fundamentally an issue of power\[[12](https://arxiv.org/html/2605.13318#bib.bib62)\]\. Moreover, the process usually has many hidden choices—usually not documented—made by the curators, giving the impression that the “natural” choices were made\.

VERA\-MH addressed this in two ways\. First, after the evaluation was announced, there was a 60\-day long request for comment \(RFC\) to incorporate feedback from stakeholders including but not limited to clinicians, AI developers, people with lived experiences, advocacy groups, and policymakers\. Secondly, the evaluation is open\-source, giving the option to suggest improvements and changes in a continuous way, both on the code, and on the clinical side \(e\.g\., rubric, personas\)\.

### 6\.2Construct validity

An abstract property needs to be operationalized to be measured, using proxies that can be directly measured\[[29](https://arxiv.org/html/2605.13318#bib.bib4)\]\. The degree to which the operationalization reflects the property is calledconstruct validity\[[15](https://arxiv.org/html/2605.13318#bib.bib38)\]\. Poor construct validity might lead to hyped, exaggerated claims, and the misunderstanding of the real capabilities of the system under evaluation\. In the case of a safety evaluation, such as VERA\-MH, that could have very impactful consequences\. This is why, instead of presenting VERA\-MH as a universal mental health benchmark, it is scoped down to a single issue, i\.e\., SI\.

### 6\.3Intra\-mode Variation

Multiple design choices influence the score, including the maximum number of turns, number of personas, and maximum number of turns before the conversation is cut off\. Generated text is statistical in nature, and single runs of evaluation might not capture the model’s variation, and, following\[[41](https://arxiv.org/html/2605.13318#bib.bib30)\]’s framing, have low reliability\. For VERA\-MH, we believe that the increased number of personas and the recommendation to run at least twice per persona are enough to take care of the variation\. An upcoming work is focused on the rating stability analysis, including the effect of the number of runs per persona\.

### 6\.4Capturing Failure Modes

As\[[22](https://arxiv.org/html/2605.13318#bib.bib8)\]notes, capturing failure modes can be more powerful and informative than just benchmarking scores\. Understanding where models fail, can guide new development and highlight areas of improvements, especially important in the case of evaluations for clinical safety\. The flow\-structured rubric \(see Section[4\.2\.2](https://arxiv.org/html/2605.13318#S4.SS2.SSS2)\) of VERA\-MH allows for pinpointing exactly where a failure happened\. In the current structure, each question whose answer is "Yes", implies the lack of best practices\. By surfacing the failure, together with the result of the evaluations, it’s possible to understand what best practice\(s\) are currently lacking\. In the current structure, only the first failure is surfaced, since after an affirmative answer, the next question asked belongs to another dimension\. However, if needed, it would be possible ask every question, surfacing all the failures and maximizing actionability\. We believe this methodology to be more trustworthy than eliciting an exlanation field from the Judge\-LLM\.

### 6\.5Economic goals

Benchmarking and evaluations can be a tool to gain publicity and funding\[[25](https://arxiv.org/html/2605.13318#bib.bib5),[37](https://arxiv.org/html/2605.13318#bib.bib6),[24](https://arxiv.org/html/2605.13318#bib.bib7)\], especially when released with new models\. There are benign cases in which a new model is released with an accompanying evaluation, for example when new models saturating previous evaluations and introducing previously untested capabilities\. However, the synchronous release of a model and evaluation can be used to signal perceived quality and performance over the competitions, especially when the new model’s score is at the top, likely due to access of, and optimization for, the evaluation during training time\. VERA\-MH was not released in tandem with a specific model or product, and its open\-source nature limits the option for gameability for a single entity\. See the following Section[7\.2](https://arxiv.org/html/2605.13318#S7.SS2)for more details\.

## 7Limitations and Future Work

While VERA\-MH was developed with a socio\-technical lens\[[46](https://arxiv.org/html/2605.13318#bib.bib36)\], and with participatory methods\[[16](https://arxiv.org/html/2605.13318#bib.bib49),[20](https://arxiv.org/html/2605.13318#bib.bib50)\], and their critiques\[[1](https://arxiv.org/html/2605.13318#bib.bib48)\], in mind, there are still some limitations, which we hope will inspire directions of future work\.

### 7\.1Lack of Consensus on Best practices

As the field quickly evolves, so can the agreement of what constitutes best practices\. The rubric embodies the current understanding of state\-of\-the\-art, which might change if, for example, new consensus on best practices is achieved, new model capabilities arise, new evidence is presented, or new regulations are enacted\.

### 7\.2Open\-source and Gameability

While the decision to make the benchmark open\-source was explicit, it also comes with a cost\. The dynamic nature of the evaluation, with a fresh set of conversations generated each time, reduces the risk of memorizing or optimizing for a specific dataset\. However, the personas \(and their characteristics\) used to generate the conversations are fixed, which could lead to overfitting on them, even if with an extra level of indirection\. Reported evaluation’s result, without an independent entity verifying code’s version and hyper\-parameter, leaves the door open for gaming, including fine\-tuning against specific applications, or cherry\-picking of results\.

### 7\.3Results’ Complexity and their Usefulness

In the current set up, each system is evaluated around roughly 2000 data points \(2 conversations for each of the 100 personas, and 2 judging each, along 5 dimensions\), which could be aggregated and sliced in many ways\. Currently, we are grouping on both the dimension and the 4 options for each, producing a matrix of5⋅4=205\\cdot 4=20numbers\. While this rating maintains the most information, it makes comparisons between models harder\. Future research is needed to find the right balance between information overload, and the known pitfalls of single\-metrics\[[47](https://arxiv.org/html/2605.13318#bib.bib22)\], including Goodhart’s law\[[23](https://arxiv.org/html/2605.13318#bib.bib23),[49](https://arxiv.org/html/2605.13318#bib.bib61)\], which in our contexet cab be stated as “when an evaluation becomes a target, it ceases to be a good evaluation\.”

### 7\.4Dependence on Ancillary Models

Currently, we rely on two classes of ancillary models: one to simulate users, and one to use as an LLM\-as\-a\-Judge\. In our experiments, only proprietary closed models were used, subject to change at any time and without notice\. Even versioning models, for example via number or release date, might not fully guarantee the lack of other changes \(i\.e\., in the pre\- and post\-processing layers\) that could dramatically influence how the conversations are generated or judged\. To reduce variation, one possible direction is to use fully open\-weight models to better control their lifecycles\. Even better, open\-weight models could be fine\-tuned to represent more faithfully specific personas, thus having both more stable and better simulated conversations\. Similarly, another open\-weight model could be fine\-tuned to better match clinician judges\. However, given how often the rubric is subject to change to follow emerging best practices, it is likely not a viable solution in the short term\.

### 7\.5User simulations

Dynamic and synthetic conversations are very useful tools to evaluate chatbots\. However, the quality of resulting conversations is only as good as the LLMs are at modeling realistic users’ behaviour\. While in\[[9](https://arxiv.org/html/2605.13318#bib.bib3)\]clinicians also rated conversations for realism, we should be careful from drawing conclusions from these ratings\. It’s unclear, for example, what the gold standard of realism should be, and what the chats should compared against\. Talk therapy transcripts \(which might be what the clinicians\-raters are most familiar with\) do not represent how people interact with chatbots, as such interactions are usually much more direct\. Limitations on LLMs to when prompted to act as personas are known\[[50](https://arxiv.org/html/2605.13318#bib.bib56),[57](https://arxiv.org/html/2605.13318#bib.bib63)\], even more consequential when simulated conversations are used to determine chatbots’ safety\. Simulated users might just reinforce harmful stereotypes\[[31](https://arxiv.org/html/2605.13318#bib.bib57),[38](https://arxiv.org/html/2605.13318#bib.bib64)\], instead of representing the complexity of each of the personas\. The risk is even higher, when the personas exist at the intersection of multiple identities\. The lack of an appropriate amount of training data, or bias training data, has been known to cause bias in the pre\-LLM world, e\.g\. in classifications tasks\[[11](https://arxiv.org/html/2605.13318#bib.bib58)\]\.

Just like the judging part of VERA\-MH was investigated\[[9](https://arxiv.org/html/2605.13318#bib.bib3)\], a similar study could examine the realism of the generated conversations\. How realism is defined, what the generated conversations should be compared to, and how labelers are selected, should be the subject of careful analysis\.

### 7\.6Limitation of Personas

The number of personas was increased from an initial 10 to 100, enabling a higher diversity of users\. However, no amount of personas could capture the variety of the human experience, forcing the operationalization to pick an arbitrary number of personas\. Personas are also context dependent and not universal, as their experiences are representative of a specific cultural and social background\. We recognize that the personas in our evaluation primarily reflect a US\-based population and value systems\. Transpositions of VERA\-MH to other contexts requires adapting or creating appropriate new personas\.

### 7\.7Language

The evaluation, including the simulation and the rubrics, are currently only provided in English\. Successful localization requires careful context dependent translations \(including, but not limited to, ways in which suicide is indirectly addressed\) We caution against adopting automated translations, as those are known not to be able to capture the nuances and context of the original speech, as evidence from content moderation social media shows\[[28](https://arxiv.org/html/2605.13318#bib.bib60)\]\.

### 7\.8Computational Costs

The use of two ancillary models in the evaluation \(one for user simulation, and one as an LLM\-judge\) increases the cost exponentially\. With20002000data points required for each choice of \(user, judge\), the computational costs scales ton2n^\{2\}\. The challenge is capturing a realistic sample of at\-risk users, while balancing against the cost constraints of an evaluation with many more personas that require a much greater number of simulated and judged conversations\. For general LLMs, models under evaluation tend to use 6\-13 M input tokens and 0\.5\-1\.5 M output tokens, with the cost varying by token cost for the model\. As of May 2026, we estimate the costs for evaluation a single provider around$​220\\mathdollar 220, if using Opus 4\.5 and GPT 5\.2 as the user\-LLMs and Sonnet 4\.5 and GPT\-4o as the judge\-LLM\.

## 8Discussion

In this paper, we introduced VERA\-MH, a clinically\-developed multi\-turn evaluation to measure the safety of chatbots interacting with users who might display risk of suicidal ideation\. The open\-source evaluation relies on dynamically\-simulated conversation, rather than single prompt or scripted ones, to allow for realistic pre\-deployment model testing\. The simulations are based on 100 personas, developed in tandem with clinicians, to include a wide range of lived experiences, demographic and clinical data\. Simulated conversations are judged against the clinically developed rubric that holds the best practices on how a model should respond to users in crisis\. VERA\-MH was designed taking into account the current pitfalls of evaluation LLM\-science, and trying to address them as much as possible while offering a ready\-to\-use tool for model developers and deployers\. Its multi\-nature stakeholder is reflected in its design, and code, in which the clinical portions are clearly kept separate and accessible to anyone, regardless of their familiarity with coding\.

## References

- \[1\]W\. Agnew, A\. S\. Bergman, J\. Chien, M\. Díaz, S\. El\-Sayed, J\. Pittman, S\. Mohamed, and K\. R\. McKee\(2024\)The illusion of artificial inclusion\.InProceedings of the 2024 CHI Conference on Human Factors in Computing Systems,CHI ’24,New York, NY, USA\.External Links:ISBN 9798400703300,[Link](https://doi.org/10.1145/3613904.3642703),[Document](https://dx.doi.org/10.1145/3613904.3642703)Cited by:[§7](https://arxiv.org/html/2605.13318#S7.p1.1)\.
- \[2\]A\. Alaa, T\. Hartvigsen, N\. Golchini, S\. Dutta, F\. Dean, I\. D\. Raji, and T\. Zack\(2025\)Position: medical large language model benchmarks should prioritize construct validity\.InForty\-second International Conference on Machine Learning Position Paper Track,External Links:[Link](https://openreview.net/forum?id=YuMEUNNpeb)Cited by:[§2](https://arxiv.org/html/2605.13318#S2.p1.1)\.
- \[3\]R\. K\. Arora, J\. Wei, R\. S\. Hicks, P\. Bowman, J\. Quiñonero\-Candela, F\. Tsimpourlas, M\. Sharman, M\. Shah, A\. Vallone, A\. Beutel, J\. Heidecke, and K\. Singhal\(2025\)HealthBench: evaluating large language models towards improved human health\.External Links:2505\.08775,[Link](https://arxiv.org/abs/2505.08775)Cited by:[§2](https://arxiv.org/html/2605.13318#S2.p3.1)\.
- \[4\]A\. Badawi, E\. Rahimi, M\. T\. R\. Laskar, S\. Grach, L\. Bertrand, L\. Danok, P\. Dhanesh, J\. Huang, F\. Rudzicz, and E\. Dolatabadi\(2026\-03\)When can we trust LLMs in mental health? large\-scale benchmarks for reliable LLM evaluation\.InProceedings of the 19th Conference of the European Chapter of the Association for Computational Linguistics \(Volume 1: Long Papers\),V\. Demberg, K\. Inui, and L\. Marquez \(Eds\.\),Rabat, Morocco,pp\. 3873–3896\.External Links:[Link](https://aclanthology.org/2026.eacl-long.180/),[Document](https://dx.doi.org/10.18653/v1/2026.eacl-long.180),ISBN 979\-8\-89176\-380\-7Cited by:[§2](https://arxiv.org/html/2605.13318#S2.p2.1)\.
- \[5\]N\. Badshah\(2026\)Teenager died after asking chatgpt for ‘most successful’ way to take his life, inquest told\(Website\)External Links:[Link](https://www.theguardian.com/society/2026/mar/31/teenager-asked-chatgpt-most-successful-ways-take-life-inquest-told)Cited by:[§1](https://arxiv.org/html/2605.13318#S1.p2.1)\.
- \[6\]J\. Batzner, L\. Choshen, A\. Ghosh, S\. H\. Nelaturu, A\. Kornilova, D\. Stachura, Y\. Mai, A\. Yehudai, A\. Reuel, I\. Solaiman, and S\. Biderman\(2026\-02\)Every eval ever: toward a common language for ai eval reporting\.Note:Blog Post, EvalEval CoalitionExternal Links:[Link](https://evalevalai.com/infrastructure/2026/02/17/everyevalever-launch/)Cited by:[§2](https://arxiv.org/html/2605.13318#S2.p1.1)\.
- \[7\]A\. M\. Bean, R\. O\. Kearns, A\. Romanou, F\. S\. Hafner, H\. Mayne, J\. Batzner, N\. Foroutan, C\. Schmitz, K\. Korgul, H\. Batra, O\. Deb, E\. Beharry, C\. Emde, T\. Foster, A\. Gausen, M\. Grandury, S\. Han, V\. Hofmann, L\. Ibrahim, H\. Kim, H\. R\. Kirk, F\. Lin, G\. K\. Liu, L\. Luettgau, J\. Magomere, J\. Rystrøm, A\. Sotnikova, Y\. Yang, Y\. Zhao, A\. Bibi, A\. Bosselut, R\. Clark, A\. Cohan, J\. N\. Foerster, Y\. Gal, S\. A\. Hale, I\. D\. Raji, C\. Summerfield, P\. Torr, C\. Ududec, L\. Rocher, and A\. Mahdi\(2026\)Measuring what matters: construct validity in large language model benchmarks\.InThe Thirty\-ninth Annual Conference on Neural Information Processing Systems Datasets and Benchmarks Track,External Links:[Link](https://openreview.net/forum?id=mdA5lVvNcU)Cited by:[§2](https://arxiv.org/html/2605.13318#S2.p1.1),[§6](https://arxiv.org/html/2605.13318#S6.p1.1)\.
- \[8\]L\. Belli, K\. Bentley, W\. Alexander, E\. Ward, M\. Hawrilenko, K\. Johnston, M\. Brown, and A\. Chekroud\(2026\)VERA\-mh concept paper\.External Links:2510\.15297,[Link](https://arxiv.org/abs/2510.15297)Cited by:[§1](https://arxiv.org/html/2605.13318#S1.p3.1),[§4\.1\.1](https://arxiv.org/html/2605.13318#S4.SS1.SSS1.p1.1)\.
- \[9\]K\. H\. Bentley, L\. Belli, A\. M\. Chekroud, E\. J\. Ward, E\. R\. Dworkin, E\. V\. Ark, K\. M\. Johnston, W\. Alexander, M\. Brown, and M\. Hawrilenko\(2026\)VERA\-mh: reliability and validity of an open\-source ai safety evaluation in mental health\.External Links:2602\.05088,[Link](https://arxiv.org/abs/2602.05088)Cited by:[§4\.2\.2](https://arxiv.org/html/2605.13318#S4.SS2.SSS2.p3.1),[§4\.2\.3](https://arxiv.org/html/2605.13318#S4.SS2.SSS3.p1.1),[§7\.5](https://arxiv.org/html/2605.13318#S7.SS5.p1.1),[§7\.5](https://arxiv.org/html/2605.13318#S7.SS5.p2.1)\.
- \[10\]C\. R\. Blease and J\. B\. Torous\(2023\)ChatGPT and mental healthcare: balancing benefits with risks of harms\.BMJ Mental Health26\.External Links:[Link](https://api.semanticscholar.org/CorpusID:265121706)Cited by:[§1](https://arxiv.org/html/2605.13318#S1.p1.1)\.
- \[11\]D\. Borkan, L\. Dixon, J\. Sorensen, N\. Thain, and L\. Vasserman\(2019\)Nuanced metrics for measuring unintended bias with real data for text classification\.InCompanion Proceedings of The 2019 World Wide Web Conference,WWW ’19,New York, NY, USA,pp\. 491–500\.External Links:ISBN 9781450366755,[Link](https://doi.org/10.1145/3308560.3317593),[Document](https://dx.doi.org/10.1145/3308560.3317593)Cited by:[§7\.5](https://arxiv.org/html/2605.13318#S7.SS5.p1.1)\.
- \[12\]D\. Boyd and K\. Crawford\(2012\)CRITICAL questions for big data\.Information, Communication & Society15,pp\. 662 – 679\.External Links:[Link](https://api.semanticscholar.org/CorpusID:51843165)Cited by:[§6\.1](https://arxiv.org/html/2605.13318#S6.SS1.p1.1)\.
- \[13\]R\. Chatterjee\(2025\)Their teenage sons died by suicide\. now, they are sounding an alarm about ai chatbots\(Website\)External Links:[Link](https://www.npr.org/sections/shots-health-news/2025/09/19/nx-s1-5545749/ai-chatbots-safety-openai-meta-characterai-teens-suicide)Cited by:[§1](https://arxiv.org/html/2605.13318#S1.p2.1)\.
- \[14\]K\. W\. Crenshaw\(1991\)Mapping the margins: intersectionality, identity politics, and violence against women of color\.Stanford Law Review43,pp\. 1241–1299\.External Links:[Link](https://api.semanticscholar.org/CorpusID:24661090)Cited by:[§4\.1\.1](https://arxiv.org/html/2605.13318#S4.SS1.SSS1.p1.1)\.
- \[15\]L\. J\. Cronbach and P\. E\. Meehl\(1955\)Construct validity in psychological tests\.\.Psychological bulletin52 4,pp\. 281–302\.External Links:[Link](https://api.semanticscholar.org/CorpusID:5312179)Cited by:[§6\.2](https://arxiv.org/html/2605.13318#S6.SS2.p1.1)\.
- \[16\]F\. Delgado, S\. Yang, M\. Madaio, and Q\. Yang\(2023\)The participatory turn in ai design: theoretical foundations and the current state of practice\.Proceedings of the 3rd ACM Conference on Equity and Access in Algorithms, Mechanisms, and Optimization\.External Links:[Link](https://api.semanticscholar.org/CorpusID:263605822)Cited by:[§7](https://arxiv.org/html/2605.13318#S7.p1.1)\.
- \[17\]B\. Dwyer, M\. Flathers, A\. Sano, A\. Dempsey, A\. Cipriani, A\. H\. Gazi, B\. Hill, C\. Gorban, C\. I\. Rodriguez, C\. Stromeyer, D\. King, E\. Rozenblit, G\. Strudwick, J\. Linardon, J\. Cheong, J\. Firth, J\. Herpertz, J\. Schwarz, K\. T\. Truong, M\. Emerson, M\. P\. Paulus, M\. Patriquin, Y\. Hua, S\. Choudhary, S\. Siddals, L\. O\. Pinillos, J\. Bantjes, S\. Scheuller, X\. Xu, K\. Duckworth, D\. H\. Gillison, M\. Wood, and J\. B\. Torous\(2025\)Mindbench\.ai: an actionable platform to evaluate the profile and performance of large language models in a mental healthcare context\.NPP \- Digital Psychiatry and Neuroscience3\.External Links:[Link](https://api.semanticscholar.org/CorpusID:283026485)Cited by:[§2](https://arxiv.org/html/2605.13318#S2.p2.1)\.
- \[18\]M\. Eriksson, E\. Purificato, A\. Noroozian, J\. Vinagre, G\. Chaslot, E\. Gomez, and D\. Fernandez\-Llorca\(2025\-Oct\.\)Can we trust ai benchmarks? an interdisciplinary review of current issues in ai evaluation\.Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society8\(1\),pp\. 850–864\.External Links:[Link](https://ojs.aaai.org/index.php/AIES/article/view/36595),[Document](https://dx.doi.org/10.1609/aies.v8i1.36595)Cited by:[§2](https://arxiv.org/html/2605.13318#S2.p1.1),[§6](https://arxiv.org/html/2605.13318#S6.p1.1)\.
- \[19\]C\. for AI Standards and Innovation/NIST\(2026\)Practices for automated benchmark evaluations of language models\.External Links:[Link](https://nvlpubs.nist.gov/nistpubs/ai/NIST.AI.800-2.ipd.pdf)Cited by:[§2](https://arxiv.org/html/2605.13318#S2.p1.1)\.
- \[20\]T\. E\. C\. for Not\-for\-Profit Law Stichting \(ECNL\) and SocietyInside\(2025\)Framework for meaningful engagement 2\.0\.External Links:[Link](https://ecnl.org/sites/default/files/2025-11/ECNL%20Framework%20Meaningful%20Engagement%202.0%202025.pdf)Cited by:[§7](https://arxiv.org/html/2605.13318#S7.p1.1)\.
- \[21\]A\. F\. for Suicide Prevention\(2024\)Suicide statistics\(Website\)External Links:[Link](https://afsp.org/suicide-statistics/)Cited by:[§1](https://arxiv.org/html/2605.13318#S1.p2.1)\.
- \[22\]S\. Gehrmann, E\. Clark, and T\. Sellam\(2023\-06\)Repairing the cracked foundation: a survey of obstacles in evaluation practices for generated text\.J\. Artif\. Int\. Res\.77\.External Links:ISSN 1076\-9757,[Link](https://doi.org/10.1613/jair.1.13715),[Document](https://dx.doi.org/10.1613/jair.1.13715)Cited by:[§6\.4](https://arxiv.org/html/2605.13318#S6.SS4.p1.1)\.
- \[23\]C\. A\. E\. Goodhart\(1984\)Problems of monetary management: the uk experience\.External Links:[Link](https://api.semanticscholar.org/CorpusID:168522062)Cited by:[§7\.3](https://arxiv.org/html/2605.13318#S7.SS3.p1.1)\.
- \[24\]G\. Grill\(2024\)Constructing capabilities: the politics of testing infrastructures for generative ai\.InProceedings of the 2024 ACM Conference on Fairness, Accountability, and Transparency,FAccT ’24,New York, NY, USA,pp\. 1838–1849\.External Links:ISBN 9798400704505,[Link](https://doi.org/10.1145/3630106.3659009),[Document](https://dx.doi.org/10.1145/3630106.3659009)Cited by:[§2](https://arxiv.org/html/2605.13318#S2.p1.1),[§6\.5](https://arxiv.org/html/2605.13318#S6.SS5.p1.1),[§6](https://arxiv.org/html/2605.13318#S6.p1.1)\.
- \[25\]A\. Hardy, A\. Reuel, K\. Jafari Meimandi, L\. Soder, A\. Griffith, D\. M\. Asmar, S\. Koyejo, M\. S\. Bernstein, and M\. J\. Kochenderfer\(2025\)More than marketing? on the information value of ai benchmarks for practitioners\.InProceedings of the 30th International Conference on Intelligent User Interfaces,IUI ’25,New York, NY, USA,pp\. 1032–1047\.External Links:ISBN 9798400713064,[Link](https://doi.org/10.1145/3708359.3712152),[Document](https://dx.doi.org/10.1145/3708359.3712152)Cited by:[§6\.5](https://arxiv.org/html/2605.13318#S6.SS5.p1.1)\.
- \[26\]M\. Holmes, T\. Lacerda, and R\. Schwartz\(2026\)Making ai evaluation deployment relevant through context specification\.External Links:2603\.06811,[Link](https://arxiv.org/abs/2603.06811)Cited by:[§2](https://arxiv.org/html/2605.13318#S2.p1.1)\.
- \[27\]Y\. Hua, H\. Na, Z\. Li, F\. Liu, X\. Fang, D\. A\. Clifton, and J\. B\. Torous\(2025\)A scoping review of large language models for generative tasks in mental health care\.NPJ Digital Medicine8\.External Links:[Link](https://api.semanticscholar.org/CorpusID:278235350)Cited by:[§1](https://arxiv.org/html/2605.13318#S1.p1.1)\.
- \[28\]A\. International\(2022\)The social atrocity: meta and the right to remedy for the rohingya\.External Links:[Link](https://www.amnesty.org/en/documents/ASA16/5933/2022/en/)Cited by:[§7\.7](https://arxiv.org/html/2605.13318#S7.SS7.p1.1)\.
- \[29\]A\. Z\. Jacobs and H\. Wallach\(2021\)Measurement and fairness\.InProceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency,FAccT ’21,New York, NY, USA,pp\. 375–385\.External Links:ISBN 9781450383097,[Link](https://doi.org/10.1145/3442188.3445901),[Document](https://dx.doi.org/10.1145/3442188.3445901)Cited by:[§6\.2](https://arxiv.org/html/2605.13318#S6.SS2.p1.1)\.
- \[30\]A\. Kang, J\. Y\. Chen, Z\. Lee\-Youngzie, and S\. Fu\(2024\)Synthetic data generation with llm for improved depression prediction\.ArXivabs/2411\.17672\.External Links:[Link](https://api.semanticscholar.org/CorpusID:274281371)Cited by:[§2](https://arxiv.org/html/2605.13318#S2.p2.1)\.
- \[31\]A\. Kantharuban, J\. Milbauer, E\. Strubell, and G\. Neubig\(2024\)Stereotype or personalization? user identity biases chatbot recommendations\.ArXivabs/2410\.05613\.External Links:[Link](https://api.semanticscholar.org/CorpusID:273228304)Cited by:[§7\.5](https://arxiv.org/html/2605.13318#S7.SS5.p1.1)\.
- \[32\]R\. A\. Kleinman, J\. B\. Torous, and M\. Danilewitz\(2026\)Use of large\-language models for therapy: promise and perils\.\.Annals of internal medicine\.External Links:[Link](https://api.semanticscholar.org/CorpusID:285658847)Cited by:[§1](https://arxiv.org/html/2605.13318#S1.p1.1)\.
- \[33\]R\. K\. McBain, R\. Bozick, M\. Diliberti, L\. A\. Zhang, F\. Zhang, A\. Burnett, A\. Kofner, B\. Rader, J\. Breslau, B\. D\. Stein, A\. Mehrotra, L\. U\. Pines, J\. Cantor, and H\. Yu\(2025\-11\)Use of generative ai for mental health advice among us adolescents and young adults\.JAMA Network Open8\(11\),pp\. e2542281–e2542281\.External Links:ISSN 2574\-3805,[Document](https://dx.doi.org/10.1001/jamanetworkopen.2025.42281),[Link](https://doi.org/10.1001/jamanetworkopen.2025.42281),https://jamanetwork\.com/journals/jamanetworkopen/articlepdf/2841067/mcbain\_2025\_ld\_250258\_1761924421\.38077\.pdfCited by:[§1](https://arxiv.org/html/2605.13318#S1.p1.1)\.
- \[34\]Cited by:[§1](https://arxiv.org/html/2605.13318#S1.p2.1)\.
- \[35\]J\. Moore, D\. Grabb, W\. Agnew, K\. Klyman, S\. Chancellor, D\. C\. Ong, and N\. Haber\(2025\)Expressing stigma and inappropriate responses prevents llms from safely replacing mental health providers\.\.InProceedings of the 2025 ACM Conference on Fairness, Accountability, and Transparency,FAccT ’25,New York, NY, USA,pp\. 599–627\.External Links:ISBN 9798400714825,[Link](https://doi.org/10.1145/3715275.3732039),[Document](https://dx.doi.org/10.1145/3715275.3732039)Cited by:[§2](https://arxiv.org/html/2605.13318#S2.p2.1),[§2](https://arxiv.org/html/2605.13318#S2.p3.1)\.
- \[36\]A\. O’Dowd\(2025\)ChatGPT: more than a million users show signs of mental health distress and mania each week, internal data suggest\.BMJ391\.External Links:[Document](https://dx.doi.org/10.1136/bmj.r2290),[Link](https://www.bmj.com/content/391/bmj.r2290),https://www\.bmj\.com/content/391/bmj\.r2290\.full\.pdfCited by:[§1](https://arxiv.org/html/2605.13318#S1.p2.1)\.
- \[37\]W\. Orr and E\. B\. Kang\(2024\)AI as a sport: on the competitive epistemologies of benchmarking\.InProceedings of the 2024 ACM Conference on Fairness, Accountability, and Transparency,FAccT ’24,New York, NY, USA,pp\. 1875–1884\.External Links:ISBN 9798400704505,[Link](https://doi.org/10.1145/3630106.3659012),[Document](https://dx.doi.org/10.1145/3630106.3659012)Cited by:[§6\.5](https://arxiv.org/html/2605.13318#S6.SS5.p1.1)\.
- \[38\]R\. Ostrow and A\. Lopez\(2025\)LLMs reproduce stereotypes of sexual and gender minorities\.External Links:2501\.05926,[Link](https://arxiv.org/abs/2501.05926)Cited by:[§4\.1\.1](https://arxiv.org/html/2605.13318#S4.SS1.SSS1.p1.1),[§7\.5](https://arxiv.org/html/2605.13318#S7.SS5.p1.1)\.
- \[39\]V\. S\. P and M\. Rao\(2024\)PsychSynth: advancing mental health ai through synthetic data generation and curriculum training\.In2024 9th International Conference on Computer Science and Engineering \(UBMK\),Vol\.,pp\. 1–6\.External Links:[Document](https://dx.doi.org/10.1109/UBMK63289.2024.10773545)Cited by:[§2](https://arxiv.org/html/2605.13318#S2.p2.1)\.
- \[40\]J\. Pombal, M\. D’Eon, N\. M\. Guerreiro, P\. H\. Martins, A\. Farinhas, and R\. Rei\(2025\)MindEval: benchmarking language models on multi\-turn mental health support\.External Links:2511\.18491,[Link](https://arxiv.org/abs/2511.18491)Cited by:[§2](https://arxiv.org/html/2605.13318#S2.p3.1)\.
- \[41\]S\. Rabanser, S\. Kapoor, P\. Kirgis, K\. Liu, S\. Utpala, and A\. Narayanan\(2026\)Towards a science of ai agent reliability\.External Links:2602\.16666,[Link](https://arxiv.org/abs/2602.16666)Cited by:[§2](https://arxiv.org/html/2605.13318#S2.p1.1),[§6\.3](https://arxiv.org/html/2605.13318#S6.SS3.p1.1)\.
- \[42\]D\. Raji, E\. Denton, E\. M\. Bender, A\. Hanna, and A\. Paullada\(2021\)AI and the everything in the whole wide world benchmark\.InProceedings of the Neural Information Processing Systems Track on Datasets and Benchmarks,J\. Vanschoren and S\. Yeung \(Eds\.\),Vol\.1,pp\.\.External Links:[Link](https://datasets-benchmarks-proceedings.neurips.cc/paper_files/paper/2021/file/084b6fbb10729ed4da8c3d3f5a3ae7c9-Paper-round2.pdf)Cited by:[§2](https://arxiv.org/html/2605.13318#S2.p1.1),[§6](https://arxiv.org/html/2605.13318#S6.p1.1)\.
- \[43\]I\. D\. Raji, R\. Daneshjou, and E\. Alsentzer\(2025\)It’s time to bench the medical exam benchmark\.NEJM AI\.External Links:[Link](https://api.semanticscholar.org/CorpusID:275866409)Cited by:[§2](https://arxiv.org/html/2605.13318#S2.p1.1),[§6](https://arxiv.org/html/2605.13318#S6.p1.1)\.
- \[44\]M\. Rauh, N\. Marchal, A\. Manzini, L\. A\. Hendricks, R\. Comanescu, C\. Akbulut, T\. Stepleton, J\. Mateos\-Garcia, S\. Bergman, J\. Kay, C\. Griffin, B\. Bariach, I\. Gabriel, V\. Rieser, W\. Isaac, and L\. Weidinger\(2024\-Oct\.\)Gaps in the safety evaluation of generative ai\.Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society7\(1\),pp\. 1200–1217\.External Links:[Link](https://ojs.aaai.org/index.php/AIES/article/view/31717),[Document](https://dx.doi.org/10.1609/aies.v7i1.31717)Cited by:[§2](https://arxiv.org/html/2605.13318#S2.p1.1)\.
- \[45\]E\. L\. Rob Kuznia\(2025\)‘You’re not rushing\. you’re just ready:’ parents say chatgpt encouraged son to kill himself\(Website\)External Links:[Link](https://edition.cnn.com/2025/11/06/us/openai-chatgpt-suicide-lawsuit-invs-vis)Cited by:[§1](https://arxiv.org/html/2605.13318#S1.p2.1)\.
- \[46\]A\. D\. Selbst, D\. Boyd, S\. A\. Friedler, S\. Venkatasubramanian, and J\. Vertesi\(2019\)Fairness and abstraction in sociotechnical systems\.InProceedings of the Conference on Fairness, Accountability, and Transparency,FAT\* ’19,New York, NY, USA,pp\. 59–68\.External Links:ISBN 9781450361255,[Link](https://doi.org/10.1145/3287560.3287598),[Document](https://dx.doi.org/10.1145/3287560.3287598)Cited by:[§7](https://arxiv.org/html/2605.13318#S7.p1.1)\.
- \[47\]S\. Singh, Y\. Nan, A\. Wang, D\. D’souza, S\. Kapoor, A\. Üstün, S\. Koyejo, Y\. Deng, S\. Longpre, N\. A\. Smith, B\. Ermis, M\. Fadaee, and S\. Hooker\(2026\)The leaderboard illusion\.InThe Thirty\-ninth Annual Conference on Neural Information Processing Systems Datasets and Benchmarks Track,External Links:[Link](https://openreview.net/forum?id=4Ae8edNqm0)Cited by:[§2](https://arxiv.org/html/2605.13318#S2.p1.1),[§7\.3](https://arxiv.org/html/2605.13318#S7.SS3.p1.1)\.
- \[48\]H\. Song, M\. Kang, J\. Shin, J\. Kim, C\. Park, H\. Yoo, J\. An, A\. Oh, J\. Han, and K\. Lim\(2026\)MentalBench: a benchmark for evaluating psychiatric diagnostic capability of large language models\.External Links:2602\.12871,[Link](https://arxiv.org/abs/2602.12871)Cited by:[§2](https://arxiv.org/html/2605.13318#S2.p3.1)\.
- \[49\]R\. L\. Thomas and D\. Uminsky\(2022\)Reliance on metrics is a fundamental challenge for ai\.Patterns3\(5\),pp\. 100476\.External Links:ISSN 2666\-3899,[Document](https://dx.doi.org/https%3A//doi.org/10.1016/j.patter.2022.100476),[Link](https://www.sciencedirect.com/science/article/pii/S2666389922000563)Cited by:[§7\.3](https://arxiv.org/html/2605.13318#S7.SS3.p1.1)\.
- \[50\]P\. N\. Venkit, J\. Li, Y\. Zhou, S\. M\. Rajtmajer, and S\. Wilson\(2026\)A tale of two identities: an ethical audit of ai\-crafted synthetic personas\.InAAAI Conference on Artificial Intelligence,External Links:[Link](https://api.semanticscholar.org/CorpusID:286675172)Cited by:[§7\.5](https://arxiv.org/html/2605.13318#S7.SS5.p1.1)\.
- \[51\]R\. Wang, S\. Milani, J\. C\. Chiu, J\. Zhi, S\. M\. Eack, T\. Labrum, S\. M\. Murphy, N\. Jones, K\. V\. Hardy, H\. Shen, F\. Fang, and Z\. Chen\(2024\-11\)PATIENT\-ψ\\psi: using large language models to simulate patients for training mental health professionals\.InProceedings of the 2024 Conference on Empirical Methods in Natural Language Processing,Y\. Al\-Onaizan, M\. Bansal, and Y\. Chen \(Eds\.\),Miami, Florida, USA,pp\. 12772–12797\.External Links:[Link](https://aclanthology.org/2024.emnlp-main.711/),[Document](https://dx.doi.org/10.18653/v1/2024.emnlp-main.711)Cited by:[§2](https://arxiv.org/html/2605.13318#S2.p2.1)\.
- \[52\]A\. Warner, J\. LeDue, Y\. Cao, J\. Tham, and T\. H\. Murphy\(2025\)Synthetic patient and interview transcript creator: an essential tool for llms in mental health\.Frontiers in Digital HealthVolume 7 \- 2025\.External Links:[Link](https://www.frontiersin.org/journals/digital-health/articles/10.3389/fdgth.2025.1625444),[Document](https://dx.doi.org/10.3389/fdgth.2025.1625444),ISSN 2673\-253XCited by:[§2](https://arxiv.org/html/2605.13318#S2.p2.1)\.
- \[53\]N\. D\. Weaver, G\. J\. Bertolacci, E\. Rosenblad, S\. Ghoba, M\. Cunningham, K\. S\. Ikuta, M\. E\. Moberg, V\. Mougin, C\. Han, E\. E\. Wool, Y\. Abate, H\. O\. Adewuyi, Q\. E\. S\. Adnani, L\. A\. Adzigbli, A\. A\. Afolabi, S\. B\. Agampodi, B\. O\. Ahinkorah, A\. Ahmad, D\. Ahmad, S\. Ahmad, A\. Ahmed, H\. Ahmed, H\. A\. Hamad, Y\. A\. Al\-Ajlouni, R\. M\. Al\-amer, M\. ALBashtawy, W\. A\. Aldhaleei, S\. S\. S\. Ali, W\. Ali, M\. A\. Alomari, M\. A\. Alsabri, N\. Alvis\-Guzmán, Y\. M\. Al\-Worafi, A\. Amindarolzarbi, S\. Amiri, T\. Andrei, S\. Anvari, J\. Arabloo, D\. Areda, A\. A\. Artamonov, T\. Ashraf, S\. S\. Athari, M\. M\. W\. Atout, A\. Y\. Azzam, A\. D\. Badiye, N\. Baghcheghi, S\. Bahramian, M\. Banach, S\. L\. Barker\-Collo, T\. W\. Bärnighausen, A\. Barrow, A\. Bashiri, H\. A\. Bashiru, M\. Bastan, K\. Batra, R\. Batra, M\. Bayati, C\. Benjet, H\. Benzian, P\. Bertuccio, A\. S\. Bhagavathula, P\. Bhattacharjee, C\. B\. Bills, S\. H\. Boppana, G\. J\. Borges, H\. Borhany, Y\. K\. Bustanji, F\. L\. C\. dos Santos, G\. Castelpietra, A\. Caye, M\. Cenderadewi, R\. M\. Chandika, E\. K\. Chandrasekar, P\. Charalampous, Y\. Chen, R\. Chimoriya, H\. Chopra, S\. G\. Choudhari, D\. T\. Chu, I\. S\. Chukwu, M\. Chutiyami, R\. G\. Cowden, B\. A\. Dachew, O\. Dadras, X\. Dai, K\. Dalal, L\. Dandona, R\. Dandona, S\. D\. Darcho, R\. D\. C\. Soltani, C\. A\. Dávila\-Cervantes, A\. de la Torre\-Luque, S\. Debopadhaya, L\. Degenhardt, I\. Delgado\-Enciso, E\. Dervišević, M\. J\. Diaz, D\. Dongarwar, O\. P\. Doshi, H\. L\. Dsouza, S\. de Carvalho Dumith, S\. Duraisamy, E\. A\. Eboreime, F\. Efendi, M\. Ekholuenetale, R\. A\. E\. Arab, M\. Elhadi, G\. ELNahas, C\. Eltaha, S\. E\. Haque, S\. Eskandarieh, A\. Fahim, A\. Faro, A\. Fatehizadeh, P\. Fazeli, A\. Feizkhah, G\. Fekadu, N\. B\. Ferreira, F\. Fischer, R\. C\. Franklin, N\. Fridayani, M\. Gajdács, A\. P\. Gandhi, B\. Ganesan, M\. W\. Gebregergis, M\. Gebrehiwot, T\. G\. Gebremeskel, M\. Getie, D\. J\. Ghadimi, K\. Y\. Ghailan, A\. Ghashghaee, A\. Gholamrezanezhad, P\. Goleij, A\. Grada, M\. Grivna, S\. Guan, S\. Gulati, S\. Gupta, R\. A\. Gutiérrez, R\. S\. Gutiérrez\-Murillo, E\. B\. Hamilton, N\. Hanifi, I\. Hasan, M\. S\. H\. Z\. Tabatabaei, S\. I\. Hay, M\. Heidari, M\. Hemmati, N\. Q\. Hoan, M\. Hosseinzadeh, S\. Hostiuc, Junjie\. Huang, H\. H\. Huynh, S\. E\. Ibitoye, O\. S\. Ilesanmi, I\. Ilic, M\. Ilic, M\. Immurana, A\. Inok, C\. D\. Iwu, H\. Jahrami, S\. Jaka, R\. J\. Yengejeh, Z\. Ji, S\. Jin, N\. Joseph, C\. E\. Joshua, J\. J\. Jozwiak, Z\. Kabir, V\. Kadashetti, K\. K\. Kanmodi, R\. S\. Kantar, N\. Kapoor, I\. M\. Karaye, S\. Karmakar, H\. Kaur, J\. A\. Kerr, H\. Khajuria, A\. Khan, K\. Khatab, K\. A\. Kheirallah, K\. Kim, M\. S\. Kim, S\. K\. K\. Shivakumar, A\. Kolahi, H\. R\. Koohestani, V\. Krishna, N\. Kugbey, M\. Kulimbet, G\. Kumar, M\. M\. Kumar, S\. Kundu, V\. Kytö, I\. Landires, N\. H\. H\. Le, D\. W\. Lee, W\. C\. Lee, Y\. H\. Lee, S\. S\. Lim, J\. Lin, R\. T\. Liu, J\. F\. López\-Gil, G\. Lucchetti, Z\. F\. Ma, V\. Maled, K\. Malhotra, A\. A\. Malik, A\. M\. Marconi, R\. Martinez\-Piedra, R\. R\. Marzo, Y\. Mathangasinghe, P\. K\. Maulik, H\. N\. Meles, R\. G\. Menezes, T\. Meretoja, T\. Meštrović, I\. M\. Michalek, T\. R\. Miller, M\. Mirza, A\. Misganaw, C\. Mittal, A\. Z\. Mohamed, N\. S\. Mohamed, A\. Mohammadian\-Hafshejani, A\. H\. Mokdad, S\. Molinaro, L\. Monasta, A\. M\. Ghalibaf, S\. D\. Morrison, R\. Motappa, F\. Mughal, F\. Mulita, Y\. Munkhsaikhan, C\. J\. L\. Murray, S\. Muthu, W\. Myung, A\. Nafei, P\. Naghavi, G\. R\. Naik, G\. Naik, Z\. S\. Natto, M\. Naveed, S\. Navid, B\. P\. Nayak, A\. Nazri\-Panjaki, H\. B\. Netsere, S\. P\. Neupane, H\. P\. Nguyen, N\. N\. Y\. Nguyen, P\. T\. Nguyen, P\. T\. Nguyen, V\. T\. Nguyen, A\. Nikoobar, I\. Noguer, S\. Nomura, C\. A\. Nri\-Ezedi, V\. Núñez\-Samudio, O\. J\. Nzoputam, B\. Oancea, M\. S\. Oduro, I\. Oh, S\. R\. Okeke, Y\. D\. Oluwafemi, S\. Ong, M\. Ordak, H\. M\. Orpana, E\. Ortiz\-Prado, U\. L\. Osuagwu, A\. Padrón\-Monedero, J\. R\. Padubidri, R\. F\. Palma\-Álvarez, A\. Pandey, A\. Pandey, I\. Pantazopoulos, S\. Park, S\. Park, A\. Pashaei, J\. S\. Patel, S\. Pawar, P\. Peprah, M\. F\. P\. Peres, I\. Petcu, A\. K\. Philip, M\. R\. Phillips, Z\. Z\. Piracha, J\. Pradhan, E\. J\. S\. Prates, D\. R\. A\. Pribadi, J\. Puvvula, I\. Qattea, G\. Qian, V\. Radhakrishnan, P\. R\. Raghav, S\. Rahimibarghani, A\. Rahimi‐Movaghar, V\. Rahimi\-Movaghar, Md\. M\. Rahman, M\. Rahman, M\. A\. Rahman, M\. Rahmanian, P\. L\. Rajpoot, M\. M\. Ramadan, S\. K\. Ramasamy, S\. Rani, M\. Rao, S\. J\. Rao, M\. M\. Rashidi, P\. Rastogi, D\. Rathish, D\. L\. Rawaf, L\. Reifels, M\. Rezaeian, T\. G\. Rhee, J\. Rickard, L\. Roever, M\. K\. K\. Rony, C\. S\. N, B\. Saddik, F\. Sadeghian, M\. R\. Saeb, U\. Saeed, S\. S\. Moghaddam, M\. Safari, D\. Sagoe, N\. S\. Sharif‐Askari, P\. M\. Sahoo, S\. S\. Sahoo, P\. Salamati, D\. Salihu, S\. Salimi, G\. A\. Salum, S\. Sameen, A\. M\. Samy, M\. M\. Santric\-Milicevic, C\. Sarkar, G\. S\. Sarode, S\. C\. Sarode, B\. Sathian, A\. E\. Schumacher, M\. Šekerija, M\. H\. Semreen, S\. G\. Sepanlou, M\. Shafie, S\. Shahid, A\. Shaikh, M\. A\. Shaikh, A\. Sharifan, J\. S\. Rad, A\. Sharma, V\. Sharma, R\. A\. Sheikhi, M\. Shetty, P\. H\. Shetty, P\. K\. Shetty, V\. Shivarov, S\. Shool, P\. Singh, P\. Singh, S\. Singh, B\. Socea, D\. J\. Stein, M\. B\. Stein, J\. Sun, C\. K\. Swain, L\. Szarpak, S\. S\. T\. Y, S\. M\. Tabatabaei, C\. Tabche, M\. Tareke, M\. Temsah, C\. C\. Thum, T\. Y\. Tiruye, M\. R\. Tovani\-Palone, N\. M\. Tran, T\. Tran, N\. T\. M\. Duc, S\. J\. Tromans, T\. T\. T\. T\. Truyen, G\. M\. Tsegay, M\. Tumurkhuu, S\. Vahdati, A\. G\. Vaithinathan, P\. Valdez, T\. Vasankari, M\. Veroux, G\. Verras, M\. Vinayak, T\. Vos, M\. T\. Walde, Y\. Wang, J\. L\. L\. Ward, N\. D\. Wickramasinghe, M\. W\. Wojewodzic, R\. Yesodharan, A\. Yiğit, D\. Yin, P\. S\. F\. Yip, D\. K\. Yon, N\. Yonemoto, C\. Yu, I\. Zare, M\. G\.M\. Zeariya, H\. Zhang, C\. C\. Zhong, B\. Zhu, A\. Zhumagaliuly, and M\. Naghavi\(2025\)Global, regional, and national burden of suicide, 1990–2021: a systematic analysis for the global burden of disease study 2021\.The Lancet\. Public Health10,pp\. e189 – e202\.External Links:[Link](https://api.semanticscholar.org/CorpusID:276488756)Cited by:[§1](https://arxiv.org/html/2605.13318#S1.p2.1)\.
- \[54\]L\. Weidinger, M\. Rauh, N\. Marchal, A\. Manzini, L\. A\. Hendricks, J\. Mateos\-Garcia, S\. Bergman, J\. Kay, C\. Griffin, B\. Bariach, I\. Gabriel, V\. Rieser, and W\. S\. Isaac\(2023\)Sociotechnical safety evaluation of generative ai systems\.ArXivabs/2310\.11986\.External Links:[Link](https://api.semanticscholar.org/CorpusID:264289156)Cited by:[§2](https://arxiv.org/html/2605.13318#S2.p1.1)\.
- \[55\]J\. Xu, T\. Wei, B\. Hou, P\. Orzechowski, S\. Yang, R\. Jin, R\. Paulbeck, J\. Wagenaar, G\. Demiris, and L\. Shen\(2025\)MentalChat16K: a benchmark dataset for conversational mental health assistance\.InProceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining V\.2,KDD ’25,New York, NY, USA,pp\. 5367–5378\.External Links:ISBN 9798400714542,[Link](https://doi.org/10.1145/3711896.3737393),[Document](https://dx.doi.org/10.1145/3711896.3737393)Cited by:[§2](https://arxiv.org/html/2605.13318#S2.p2.1)\.
- \[56\]N\. Yousif\(2025\)Parents of teenager who took his own life sue openai\(Website\)External Links:[Link](https://www.bbc.com/news/articles/cgerwp7rdlvo)Cited by:[§1](https://arxiv.org/html/2605.13318#S1.p2.1)\.
- \[57\]A\. Zewail, A\. Figueroa, J\. Graham, and M\. Atari\(2026\)Moral stereotyping in large language models\.Proceedings of the National Academy of Sciences123\(10\),pp\. e2519941123\.External Links:[Document](https://dx.doi.org/10.1073/pnas.2519941123),[Link](https://www.pnas.org/doi/abs/10.1073/pnas.2519941123),https://www\.pnas\.org/doi/pdf/10\.1073/pnas\.2519941123Cited by:[§7\.5](https://arxiv.org/html/2605.13318#S7.SS5.p1.1)\.
- \[58\]L\. Zheng, W\. Chiang, Y\. Sheng, S\. Zhuang, Z\. Wu, Y\. Zhuang, Z\. Lin, Z\. Li, D\. Li, E\. P\. Xing, H\. Zhang, J\. E\. Gonzalez, and I\. Stoica\(2023\)Judging llm\-as\-a\-judge with mt\-bench and chatbot arena\.InProceedings of the 37th International Conference on Neural Information Processing Systems,NIPS ’23,Red Hook, NY, USA\.Cited by:[§4\.2](https://arxiv.org/html/2605.13318#S4.SS2.p1.1),[§4](https://arxiv.org/html/2605.13318#S4.p2.1)\.

## Appendix AOther Results

![Refer to caption](https://arxiv.org/html/2605.13318v1/images/gemini.png)Figure 2:Results of the experiments focused on Gemini models\.![Refer to caption](https://arxiv.org/html/2605.13318v1/images/GPT5.X.png)Figure 3:Results of the experiments focused on GPT5\.X family of models\.![Refer to caption](https://arxiv.org/html/2605.13318v1/images/grok.png)Figure 4:Results of the experiments focused on Grok models\.![Refer to caption](https://arxiv.org/html/2605.13318v1/images/opus.png)Figure 5:Results of the experiments focused on Claude Opus models\.![Refer to caption](https://arxiv.org/html/2605.13318v1/images/sonnet.png)Figure 6:Results of the experiments focused on Claude Sonnet models\.
## Appendix BGenerated Text Statistics

![Refer to caption](https://arxiv.org/html/2605.13318v1/images/dist.png)Figure 7:Distribution of the conversational length of both user\- and chatbot model\. Users’ responses tend to be shorter, indicating the user\-LLM correctly interpreting the instructions\.Table 2:Statistic for generated text\.

## Appendix CRubric

Table 3:Detail view of the rubric and its ratings\.