{"id":"352b4366-0578-434c-b9f9-18f37c2da0de","arxiv_id":"2508.11285","paper_version":1,"verdict":"CONDITIONAL","confidence":"HIGH","novelty_score":5.0,"correctness_risk":"medium","formal_verification":"none","parameter_count":0,"one_line_summary":"Eight LLMs show measurably different emotional tones in mental-health answers: anxiety prompts produced near-saturated fear scores, depression prompts the most sadness, and stress prompts the most optimism.","lead":"This study measured the emotional tone of answers that eight AI chatbots give to practical questions about depression, anxiety, and stress. It found that each chatbot has a distinct emotional style, and that anxiety questions drew near-universal fear labels, a relevant result as more people turn to chatbots for mental health information.","discovery_kind":"new_application","skeptic_critique":{"model":"deepseek-v4-flash","headline":"Condition-level headline (fear 0.974, sadness 0.686, optimism 0.755) rests on tweet-trained classifiers never validated on LLM-generated clinical-style text; near-ceiling fear is consistent with lexical association with the word 'anxiety'.","rationale":"I read the paper's central claim as: LLMs have distinct emotional signatures, and the mental-health condition mentioned in the prompt systematically shapes the emotional tone of responses (anxiety -> fear, depression -> sadness, stress -> optimism). The load-bearing premise for the strongest version of this claim is that the automated emotion classifiers produce valid measurements on LLM-generated clinical-style text. The reader's weakest_assumption identifies exactly this issue, and I agree. The anxiety fear mean of 0.974 is at the sigmoid ceiling and is suspiciously aligned with the lexical surface form 'anxiety'; the classifier has never been shown to distinguish between a text that expresses fear and a text that merely mentions a fear-associated condition. The same holds for sadness with 'depression' and optimism with 'stress'. This is not an external-consensus disagreement; it is an internal measurement-validity risk: the numbers in Table 2 could be driven by the classifier's training distribution rather than the responses' affective content. The model-level comparisons are somewhat safer because all models were given identical prompts, but the condition-level results—which are featured in the abstract—remain unvalidated. A human-annotation study on the actual responses, complemented by a lexical-substitution control, would settle whether the concern lands. The reader's CONDITIONAL verdict is appropriate; I would not change it. Secondary issues such as uncorrected multiple comparisons and missing release of the response corpus would further weaken quantitative claims, but they are not the single most load-bearing concern.","tokens_in":17930,"tokens_out":4036,"duration_ms":51464,"concrete_test":"Run a human validation study on a stratified random sample of 200 generated responses (50 per condition, balanced across the 8 models). Have two or three clinically informed annotators independently label each response for the same emotion categories used in the paper (fear, sadness, optimism, etc.) on a 0-1 intensity scale using a written codebook; compute inter-annotator agreement and correlate human ratings with the classifier scores. Additionally, take 30 existing depression-condition responses, replace the word 'depression' with 'anxiety' (and vice versa) leaving all other text unchanged, and rerun the fear classifier. If human-rated fear for anxiety responses is far below 0.974, or if the lexical substitution shifts fear scores by more than ~0.2 while human-rated emotion is unchanged, then the reported condition-level magnitudes are lexical artifacts and the abstract's strongest cl","verdict_should_be":"UNCHANGED","load_bearing_attack":"The strongest claim in the abstract and Table 2 is that anxiety prompts elicit mean fear 0.974, depression prompts elicit sadness 0.686, and stress prompts elicit optimism 0.755. These numbers come from two classifiers (cardiffnlp/twitter-roberta-large-emotion-latest and the Twitter-roBERTa sentiment model, Section 3.6) trained on tweets. The paper provides no validation of these instruments on the actual target domain: LLM-generated, clinical-style mental-health text. The anxiety fear mean sits at the sigmoid ceiling, and every anxiety-condition prompt contains the word 'anxiety'; the Twitter emotion model was trained on social-media text where 'anxiety' is strongly associated with fear, so the 0.974 may reflect the presence of the token 'anxiety' rather than the emotional tone of the response. The same logic applies to sadness/depression and optimism/stress. The paper's own limitation paragraph (Section 5) concedes the frameworks 'may not capture the full nuance of therapeutic communication,' but that concession is not a substitute for demonstrating they measure what is claimed on this domain. If this concern lands, the condition-level magnitudes in the abstract are unsupported. The model-level comparisons (e.g., Llama vs. Mixtral) are more robust because all models saw identical prompts, so a lexical bias would partly cancel across models; however, the headline conditional effect is the central contribution.","agreement_with_reader":"agree"},"referee_report":{"model":"deepseek-v4-flash","summary":"The paper reports an observational study of eight LLM-based systems (Claude, Copilot, Gemini, GPT-4o, GPT-4o mini, Llama, Mixtral, Perplexity) responding to 20 mental-health questions under three conditions (depression, anxiety, stress) and six demographic framings. The authors score 2,880 responses with Twitter-trained sentiment/emotion classifiers and GoEmotions-based models, then use Mann–Whitney U tests to compare emotion and sentiment scores across models, conditions, and demographics. The central claims are that each LLM has a distinct emotional signature, that the mental-health condition dramatically shapes emotional tone (anxiety fear 0.974, depression sadness 0.686, stress optimism 0.755), and that demographic framing has only marginal effects.","tokens_in":18083,"tokens_out":3293,"duration_ms":36581,"significance":"If the measurements are valid, the paper offers actionable evidence that model choice could materially affect the emotional tone of AI-provided mental-health information, and it proposes emotional profiling as a deployment criterion. The study design has genuine strengths: a relatively large corpus of 2,880 responses, eight systems queried with identical prompts within each condition, a clear question set, and statistical comparisons with reported p-values. However, the headline condition-level magnitudes rest on tweet-trained emotion classifiers that are never validated on LLM-generated clinical-style text. Because every anxiety prompt contains the word 'anxiety', every depression prompt contains 'depression', and every stress prompt contains 'stress', the near-ceiling fear score of 0.974 and the corresponding sadness/optimism contrasts are consistent with lexical triggering in the classifiers' Twitter training distributions rather than with the emotional content of the responses. This measurement-validity issue is load-bearing for the manuscript's most prominent claim, although the model-vs-model comparisons are less vulnerable because all models saw identical prompts.","major_comments":[{"comment":"The condition-level magnitudes in the abstract (fear 0.974, sadness 0.686, optimism 0.755) are computed with cardiffnlp/twitter-roberta-large-emotion-latest and the TweetEval sentiment model, both trained on social-media text. The paper provides no validation of these classifiers on the target domain: LLM-generated, clinical-style mental-health responses. Since the anxiety prompts all contain the token 'anxiety', and that token is strongly fear-associated in Twitter data, the near-ceiling fear mean is equally explained by lexical association as by the actual tone of the responses. The same confound applies to 'depression'→sadness and 'stress'→optimism. I ask the authors to provide per-condition example responses, an ablation that masks or removes the condition word, or in-domain human-annotated validation. Without this, the 'affective mirroring' interpretation in §5 is circular rather th","section":"§3.6 and Table 2"},{"comment":"Copilot and Perplexity were queried through their official web interfaces while the other six models were queried through the OpenRouter API. This entangles model identity with interface/system-prompt/version differences. Consequently, model-level comparisons involving Copilot and Perplexity (e.g., 'Perplexity highest caring', 'Copilot lowest positive sentiment') cannot be cleanly attributed to the model itself. The authors should either access all systems through a common gateway or explicitly relegate these two systems to exploratory status.","section":"§3.5 and Table 1"},{"comment":"The statistical procedure runs a very large number of Mann–Whitney U tests—focal group versus all others, for each emotion, sentiment, model, condition, and demographic category—with no multiple-comparison correction. Some reported p-values (e.g., p = 0.041 for stress anger; p = 0.049 for old-user positive sentiment) are unlikely to survive even basic FDR control. In addition, the 'focal versus all others' comparison uses overlapping observations, violating the independence assumption of the test. The authors should report effect sizes or confidence intervals and apply a correction such as Benjamini–Hochberg, or treat the demographic findings as exploratory.","section":"§3.7 and §4.2"}],"minor_comments":[{"comment":"The 'disgust' row reports an average of 0.21, while all eight model-specific values are 0.016–0.027. This appears to be a decimal/typing error and should be corrected.","section":"Table 1"},{"comment":"Reference [49] is labeled OpenAI sycophancy material, but the URL points to a WHO depression fact sheet. The reference and citation need correction.","section":"References"},{"comment":"The text says the TweetEmotion model identifies 'ten distinct emotions' but then lists eleven (anger, anticipation, disgust, fear, joy, love, optimism, pessimism, sadness, surprise, trust). Please reconcile the count.","section":"§3.6"},{"comment":"Table 1 would be easier to interpret with standard deviations or confidence intervals, particularly for the small differences among models (e.g., approval, trust, caring), which are otherwise hard to distinguish from noise.","section":"§4.1"},{"comment":"The discussion states that models 'implicitly validate users’ experiences' through emotional congruence. This causal/experiential language goes beyond what the observational data can support; the data only show classifier scores, not user perception or validation.","section":"§5"}],"recommendation":"major_revision","confidential_remarks":"The reader's central concern about classifier validity lands, and it is fixable in scope: the authors can add an in-domain validation set, a lexical-confound analysis (e.g., scoring responses with condition words removed), and more cautious wording of the condition-level conclusions. The model-vs-model comparisons are more robust and should be preserved. I would also request the raw response CSV and classifier scores as part of the revision, since the paper's central quantitative claims cannot be verified without them."},"author_rebuttal":null,"desk_editor":{"model":"deepseek-v4-flash","letter":"Short version: this is a useful empirical map, and the model-level result is probably real; the condition-level headline is not. The 2880-response grid (8 models x 3 conditions x 6 profiles x 20 questions) is a clean design, and the finding that model identity drives emotional tone far more than demographic framing is worth having. I'd trust the model comparisons because every model saw identical prompts; whatever bias the classifiers have largely cancels out across models. The demographic null result also looks believable.\n\nThe soft spot is the one the stress-test flags, and it's load-bearing for the abstract. The fear mean of 0.974 for anxiety prompts sits at the sigmoid ceiling, and every response is about a condition whose name is strongly fear-associated in the classifier's Twitter training data. The same logic applies to sadness/depression and optimism/stress. The authors use off-the-shelf tweet-trained models without any validation on LLM-generated clinical-style text. Their limitation paragraph says the frameworks 'may not capture the full nuance,' but that isn't the same as showing they discriminate real emotional content here. The condition-level magnitudes in the abstract are likely lexical echoes, not evidence of models 'mirroring' user emotion. The Section 5 interpretation goes past what the data support.\n\nSecondary issues: thousands of Mann-Whitney tests without any multiple-comparison correction, no effect sizes or confidence intervals, and no data/code release. Copilot and Perplexity went through web interfaces while the rest used the OpenRouter API, which entangles channel with model identity for those two. All fixable, but right now the quantitative precision in Tables 1 and 2 is overstated.\n\nI don't think this is a desk reject. The model-level emotional-signature differences are a genuine contribution that could survive a validated instrument, and the study fills a real gap. But it needs major revision: validate the classifiers on in-domain data (or use a validated clinical/psychological text analyzer), redo the stats with corrections and effect sizes, and release the response corpus. With those changes it could be a solid empirical reference. Without them, the headline is misleading.\n\nFor peer review: yes, send it out. The flaws are serious but standard, and the core question matters enough that a good referee cycle could turn this into a useful paper.","headline":"Systematic emotional-tone map with model-level differences that likely hold up, but the condition-level headline numbers are probably an artifact of tweet-trained classifiers keying on prompt words like 'anxiety'.","tokens_in":18732,"tokens_out":2912,"would_cite":false,"duration_ms":33219,"reading_group":"maybe","serious_thinker":"yes","would_accept_peer_review":true},"rs_alignment":null,"lean_confirmation":null,"pith_extraction":{"msc":[],"pacs":[],"model":"deepseek-v4-flash","headline":"Anxiety prompts pushed eight large language models to near-ceiling fear scores (0.974), depression prompts to elevated sadness (0.686), and stress prompts to the most optimistic responses (0.755), with model identity shifting the emotional","keywords":["large language models","mental health","emotion analysis","sentiment analysis","depression","anxiety","stress","human-AI interaction"],"falsifier":"Re-annotation check: have clinicians or trained raters score a sample of the 2,880 responses, and separately rerun the same classifiers on anxiety prompts that avoid the word 'anxiety' (for example, 'racing heart, dread, unable to relax'). If human ratings diverge from the automated scores, or the 0.974 fear score drops sharply when the trigger word is removed, the headline magnitudes are lexical artefacts; if the pattern survives both checks, the claim of condition-adaptive emotional expression is supported.","tokens_in":17695,"feed_emoji":"🧠","tokens_out":7622,"duration_ms":80777,"temperature":0.7,"pith_summary":"This paper tries to establish that the emotional tone of large language model responses to mental-health questions is not uniform: the model asked and the condition named in the query systematically change the measured sentiment and emotion, while demographic framing changes it only marginally. Across 2,880 answers from eight systems, anxiety questions produced fear scores near 0.974, depression questions produced sadness near 0.686 and the most negative sentiment, and stress questions produced the most optimistic responses at 0.755. The authors argue that each model carries a distinct emotional signature, with Mixtral expressing the most disapproval, annoyance, and sadness and Llama the most optimism and joy. A sympathetic reader would care because users seeking mental-health guidance are likely receiving different emotional registers depending on which system they happen to ask.","feed_headline":"Fear score hits 0.974 when LLMs answer anxiety queries","feed_subtitle":"Across 2,880 answers, the model and the condition set the emotional tone; the user's demographic barely matters","key_machinery":"The measuring instrument is a stack of text classifiers trained on social-media data: a RoBERTa sentiment model built through TweetEval [11], a RoBERTa-large multilabel emotion model from SuperTweetEval [6], and models trained on the GoEmotions dataset [17]. These convert each LLM answer into a vector of sentiment and emotion intensities. The experimental design multiplies 20 pragmatic questions by 6 demographic framings by 3 conditions by 8 models, producing 2,880 answers whose score vectors are compared with Mann–Whitney U tests.","core_discovery":"The authors' central discovery is that LLMs are not emotionally neutral answer engines in mental-health contexts. Using probabilistic text classifiers, they assign each generated answer scores for negative, neutral, and positive sentiment and for a set of emotions; on those scores, the model identity is a strong determinant of emotional expression, and the named condition is an even stronger one. Anxiety prompts drew fear responses near the classifier ceiling, at 0.974, while suppressing optimism and trust; depression prompts drew sadness at 0.686 and the highest negative sentiment; and stress prompts drew optimism at 0.755 with elevated joy and trust. The paper interprets this condition-dep","pith_inferences":["Because the classifiers were trained on tweets and never validated on clinical-style text, the headline condition differences may partly reflect the trigger word 'anxiety' rather than response tone; a paraphrase test that removes diagnostic labels would separate the two, and the paper's same-prompt model comparisons would likely survive such a test even if the magnitudes did not.","The paper's demographic conclusion is limited to how the query is framed in a single sentence; it does not test conversational context, self-disclosure, or intersectional identities, so a stronger bias conclusion would need those conditions.","If the emotional signatures are stable across paraphrases and over time, developers could calibrate mental-health interfaces to a desired emotional register; the study itself is a cross-sectional snapshot and does not establish that stability.","The authors note in Section 5 that the emotion-detection frameworks may miss the full nuance of therapeutic communication; paired human ratings would be the natural check on all of the automated magnitudes."],"forward_implications":["Model choice changes the emotional register a user receives: Llama was the most optimistic and joyful system, Mixtral the most disapproving, annoyed, and sad, and the differences were statistically significant.","The condition named in the query drives emotional output: anxiety prompts produce near-ceiling fear, depression prompts sadness and negative sentiment, and stress prompts optimism, joy, and trust.","Demographic framing in the query has only marginal emotional effects; the profile with no demographic information showed the least positive and most negative sentiment, but the paper reads the overall demographic influence as minimal.","Organizations building mental-health applications should emotionally profile candidate models rather than choosing on technical metrics alone, matching the model's emotional signature to the application's goals."],"supporting_citations":[{"why":"Supplies the primary sentiment-scoring RoBERTa model that assigns each response its negative, neutral, and positive scores.","marker":"[11]"},{"why":"Supplies the multilabel emotion model that produces the fear, sadness, optimism, joy, and related emotion intensities behind the headline results.","marker":"[6]"},{"why":"Supplies the GoEmotions taxonomy and training data for the additional emotion dimensions such as approval, disapproval, gratitude, and annoyance.","marker":"[17]"}],"fun_headline_variants":["Anxiety prompts push LLM fear to 0.974 ceiling","Depression ties LLM sadness to 0.686, stress to optimism 0.755","Model choice sways LLM emotions more than user demographics","Mixtral most negative, Llama most optimistic LLMs","LLM emotional tone varies more by condition than by user"],"cache_read_input_tokens":2816,"weakest_assumption_plain":"The load-bearing premise is that classifiers trained on tweets score clinical-style LLM responses the same way they score social-media text, rather than over-weighting trigger words such as 'anxiety'; if that fails, the headline condition differences are partly lexical artefacts, though the model-vs-model comparisons on identical prompts may survive.","fun_headline_variants_meta":{"raw":{"variants":["Anxiety prompts push LLM fear to 0.974 ceiling","Depression ties LLM sadness to 0.686, stress to optimism 0.755","Model choice sways LLM emotions more than user demographics","Mixtral most negative, Llama most optimistic LLMs","LLM emotional tone varies more by condition than by user"]},"model":"deepseek-v4-flash","effort":"low","cost_usd":0.001339,"raw_usage":{"total_tokens":5332,"prompt_tokens":847,"completion_tokens":4485,"prompt_tokens_details":{"cached_tokens":256},"prompt_cache_hit_tokens":256,"prompt_cache_miss_tokens":591,"completion_tokens_details":{"reasoning_tokens":4393}},"tokens_in":591,"tokens_out":4485,"duration_ms":30507,"temperature":1.0,"reasoning_tokens":4393,"cache_read_input_tokens":256,"cache_creation_input_tokens":0},"cache_creation_input_tokens":0},"created_at":"2026-08-05T20:01:42.412170+00:00","model_set":{"reader":"deepseek-v4-flash"},"falsifier":"Re-annotation check: have clinicians or trained raters score a sample of the 2,880 responses, and separately rerun the same classifiers on anxiety prompts that avoid the word 'anxiety' (for example, 'racing heart, dread, unable to relax'). If human ratings diverge from the automated scores, or the 0.974 fear score drops sharply when the trigger word is removed, the headline magnitudes are lexical artefacts; if the pattern survives both checks, the claim of condition-adaptive emotional expression is supported.","supporting_citations":[{"cited_title":"SuperTweetEval: A Challenging, Unified and Heterogeneous Benchmark for Social Media NLP Research","cited_arxiv_id":"2310.14757","evidence_quote":"Supplies the multilabel emotion model that produces the fear, sadness, optimism, joy, and related emotion intensities behind the headline results."}],"review_version":1}