{"id":"96394f7a-d445-4c3d-bf4d-8b3bbbb9d31e","arxiv_id":"2605.28037","paper_version":1,"verdict":"UNVERDICTED","confidence":"LOW","novelty_score":5.0,"correctness_risk":"unknown","formal_verification":"none","parameter_count":0,"one_line_summary":"Expressed personality in LLM dialogues is shaped by trait prompts, roles, and styles in trait-specific ways, with similar patterns in English and Japanese.","lead":"The paper finds that expressed Big Five traits in LLM dialogues depend on prompted traits plus roles and expressive styles, with trait-specific patterns. A smart generalist might read it to see why simple personality prompts often fail to produce consistent AI behavior.","discovery_kind":"extension","skeptic_critique":{"model":"grok-4.3","headline":"LLM-as-a-judge trait scoring lacks reported validation against human raters or inter-model agreement","rationale":"The reader's weakest_assumption matches the single point on which all quantitative results depend; the abstract supplies no counter-evidence that would remove this risk, so the concern remains load-bearing and the verdict should stay conditional on measurement validation.","tokens_in":1777,"tokens_out":304,"duration_ms":13608,"concrete_test":"Take a stratified sample of 120 dialogues (balanced across the 6×3×3 design), obtain independent Big-Five ratings from three human annotators using the identical rating scale and instructions given to the LLM judge, then compute mean Pearson r between human averages and LLM scores per trait; if any trait falls below r=0.65, the measurement validity is insufficient to support the trait-specific conclusions.","verdict_should_be":"CONDITIONAL","load_bearing_attack":"The central claim—that role and expressive style produce trait-specific shifts in expressed Big Five scores—rests entirely on the LLM judge's numerical outputs being faithful measures of the target utterances. No details are given on judge prompt wording, whether the judge sees the original role/style prompts, calibration against human Big-Five ratings, or even basic reliability metrics (e.g., Cronbach’s α across repeated judgments). If the judge LLM itself exhibits prompt-sensitive biases that align with the manipulated factors, the reported interactions are artifacts of the measurement instrument rather than evidence about personality expression.","agreement_with_reader":"agree"},"referee_report":{"model":"grok-4.3","summary":"The paper claims that expressed Big Five personality traits in LLM-generated dialogues are shaped by the interaction of explicit trait specifications, dialogue roles, and expressive styles in a trait-specific manner (role strongly affects Openness; style affects Conscientiousness and Agreeableness; trait specification dominates Neuroticism). This is supported by a 3×3×6 factorial experiment generating 1,080 dialogues each in English and Japanese, scored via an LLM-as-a-judge framework, with broadly similar patterns across languages.","tokens_in":1894,"tokens_out":514,"duration_ms":22255,"significance":"If the trait measurements are valid, the results demonstrate that personality prompting in LLMs is not a direct mapping but a context-dependent process, with implications for designing consistent dialogue agents. The factorial design and cross-linguistic comparison are strengths, but the absence of measurement validation limits the strength of the conclusions.","major_comments":[{"comment":"Evaluation subsection (methods): The central claims rest on LLM-as-a-judge trait scores, yet no validation against human Big-Five ratings, inter-judge agreement, or reliability metrics (e.g., Cronbach’s α) is reported. Without this, it is impossible to rule out that the reported trait-specific interactions are artifacts of the judge model’s own prompt sensitivities.","section":"Evaluation subsection (methods)"},{"comment":"Results section: The abstract and high-level claims describe trait-specific effects but supply no statistical tests, p-values, confidence intervals, or effect sizes for the interactions; this makes it impossible to assess whether the observed differences (e.g., role on Openness) exceed noise.","section":"Results section"},{"comment":"Methods (prompt details): Exact wording of the personality, role, style, and judge prompts is not provided, nor is it stated whether the judge sees the original conditioning prompts; this prevents replication and raises the possibility that judge outputs simply echo the manipulated factors.","section":"Methods"}],"minor_comments":[{"comment":"The paper should include the exact number of utterances per dialogue and how they were aggregated for scoring.","section":null},{"comment":"Cross-linguistic differences are described qualitatively; quantitative comparison (e.g., correlation of effect sizes between languages) would strengthen the claim of broad similarity.","section":null}],"recommendation":"major_revision","confidential_remarks":null},"author_rebuttal":{"model":"grok-4.3","summary":"We thank the referee for the detailed and constructive comments. We address each major comment below, indicating planned revisions where appropriate.","responses":[{"response":"We acknowledge this as a genuine limitation of the current manuscript. The LLM-as-a-judge approach follows common practice but lacks the requested validation. In the revised version we will add a dedicated paragraph in the Evaluation subsection discussing known limitations of LLM judges for personality assessment and report any internal consistency checks performed across judge runs. Full human validation against Big-Five ratings would require new annotation studies that are outside the scope of the present revision.","revision_made":"partial","referee_comment":"[Evaluation subsection (methods)] The central claims rest on LLM-as-a-judge trait scores, yet no validation against human Big-Five ratings, inter-judge agreement, or reliability metrics (e.g., Cronbach’s α) is reported. Without this, it is impossible to rule out that the reported trait-specific interactions are artifacts of the judge model’s own prompt sensitivities."},{"response":"The referee correctly identifies the absence of inferential statistics. The original manuscript emphasized descriptive patterns across the factorial design. We will revise the Results section to include appropriate statistical tests (e.g., three-way ANOVA with interaction terms), reporting p-values, confidence intervals, and effect sizes for the key trait-specific effects.","revision_made":"yes","referee_comment":"[Results section] The abstract and high-level claims describe trait-specific effects but supply no statistical tests, p-values, confidence intervals, or effect sizes for the interactions; this makes it impossible to assess whether the observed differences (e.g., role on Openness) exceed noise."},{"response":"We agree that prompt transparency is essential. The revised manuscript will include all prompt templates (personality, role, style, and judge) in a new appendix. We will also explicitly state that the judge prompt receives only the generated dialogue utterances and the evaluation instructions, without the original conditioning prompts.","revision_made":"yes","referee_comment":"[Methods] Exact wording of the personality, role, style, and judge prompts is not provided, nor is it stated whether the judge sees the original conditioning prompts; this prevents replication and raises the possibility that judge outputs simply echo the manipulated factors."}],"tokens_in":1480,"tokens_out":499,"duration_ms":40595,"standing_objections":[]},"desk_editor":{"model":"grok-4.3","letter":"The main takeaway is that explicit Big Five prompts do not fully determine expressed personality in LLM dialogues; roles and expressive styles produce additional, trait-specific shifts, and the pattern holds across English and Japanese.\n\nThe experiment uses a 6x3x3 factorial design to generate 1080 dialogues per language. That scale and the cross-linguistic check are the clearest strengths. The results separate the effects cleanly enough to show that role influences Openness most, style affects Conscientiousness and Agreeableness, and Neuroticism stays closer to the trait prompt. Even the no-trait-prompt conditions still produce distinct impressions from the other factors. This is straightforward empirical work that applies an interactionist view without overclaiming a new theory.\n\nThe soft spot is the measurement. All claims rest on an LLM judge scoring the utterances, yet the abstract supplies no judge prompt text, no human calibration, no reliability numbers, and no check for whether the judge itself is biased by the same role and style manipulations. If those details are missing in the full paper too, the trait-specific interactions could be measurement artifacts rather than evidence about personality expression.\n\nThis is for people working on prompt-controlled dialogue agents who need to know how much control they actually have. A reader already running similar experiments would get testable patterns to check.\n\nIt deserves peer review. The design is structured and the question is practical, but any referee will ask first about the judge validation and the missing statistical details.","headline":"Factorial data shows roles and styles shift LLM-expressed traits in specific ways, but the LLM judge lacks any reported validation.","tokens_in":2367,"tokens_out":367,"would_cite":false,"duration_ms":17051,"reading_group":"maybe","serious_thinker":"yes","would_accept_peer_review":true},"rs_alignment":null,"lean_confirmation":null,"pith_extraction":{"msc":[],"pacs":[],"model":"grok-4.3","headline":"Expressed Big Five traits in LLM dialogues arise from the interplay of trait prompts, dialogue roles, and expressive styles rather than prompts alone.","keywords":["personality control","Big Five traits","LLM dialogue agents","dialogue roles","expressive styles","interactionist analysis","cross-linguistic comparison"],"falsifier":"Human raters scoring the same dialogues produce trait estimates that do not match the LLM judge's scores or that erase the reported trait-specific effects of role and style.","tokens_in":2692,"feed_emoji":"🤖","tokens_out":651,"duration_ms":19953,"temperature":0.7,"pith_summary":"The paper tests whether specifying Big Five personality traits in prompts reliably produces matching expression in LLM-generated dialogue. It shows that dialogue role and expressive style also shape the perceived traits, with effects that differ by trait: roles matter most for Openness, styles for Conscientiousness and Agreeableness, and explicit specification for Neuroticism. Even absent any trait prompt, different roles and styles still produce distinct personality impressions. Experiments in both English and Japanese yield broadly similar patterns, indicating the result is not language-specific.","feed_headline":"LLM personality shaped by role and style, not prompts alone","feed_subtitle":"Trait-specific effects show that dialogue context determines how Big Five traits appear in generated conversations","key_machinery":"Factorial design that crosses six personality conditions, three dialogue roles, and three expressive-style conditions to generate and judge 1,080 dialogues per language, then scores expressed Big Five traits via LLM-as-a-judge on the target agent's utterances.","core_discovery":"Perceived personality expression in LLM agents is a context-dependent outcome of three factors acting together: explicit personality-trait specification in the prompt, the assigned dialogue role, and the chosen expressive style. These factors produce trait-specific effects, with dialogue role exerting strong influence on Openness, expressive style shaping Conscientiousness and Agreeableness, and explicit trait specification dominating Neuroticism. Social and expressive conditions alone can induce distinct personality-like impressions without any trait prompt.","pith_inferences":["Agent builders could combine role and style prompts to achieve trait expression even when direct trait specification is restricted.","The same interactionist logic may apply to other measurable attributes such as emotional tone or decision style in generated text.","Testing whether the patterns persist when the judge model differs from the generator model would clarify robustness."],"forward_implications":["Personality control in LLM agents must treat trait prompting as one input among role and style rather than a direct switch.","Without explicit trait prompts, role and style choices can still create consistent personality impressions across interactions.","Control strategies should differ by target trait: role adjustments for Openness, style adjustments for Conscientiousness and Agreeableness.","Cross-language consistency implies the same three-factor model can guide agent design in multiple languages."],"fun_headline_variants":["LLM personality expression depends on role, style and trait specification","Role influences Openness, style shapes Conscientiousness and Agreeableness in LLMs","Trait prompts dominate Neuroticism while role and style affect other traits","Role and expressive style create personality impressions without trait prompts","Context factors shape expressed Big Five traits in LLM agents beyond prompts"],"cache_read_input_tokens":2112,"weakest_assumption_plain":"An LLM judge can accurately estimate the Big Five traits actually expressed in the generated utterances.","fun_headline_variants_meta":{"raw":{"variants":["LLM personality expression depends on role, style and trait specification","Role influences Openness, style shapes Conscientiousness and Agreeableness in LLMs","Trait prompts dominate Neuroticism while role and style affect other traits","Role and expressive style create personality impressions without trait prompts","Context factors shape expressed Big Five traits in LLM agents beyond prompts"]},"model":"grok-4.3","cost_usd":0.00713,"raw_usage":{"total_tokens":3255,"prompt_tokens":752,"num_sources_used":0,"completion_tokens":89,"cost_in_usd_ticks":71303000,"prompt_tokens_details":{"text_tokens":752,"audio_tokens":0,"image_tokens":0,"cached_tokens":64},"completion_tokens_details":{"audio_tokens":0,"reasoning_tokens":2414,"accepted_prediction_tokens":0,"rejected_prediction_tokens":0}},"tokens_in":752,"tokens_out":89,"duration_ms":28417,"temperature":1.0,"reasoning_tokens":2414,"cache_read_input_tokens":64,"cache_creation_input_tokens":0},"cache_creation_input_tokens":0},"created_at":"2026-06-29T12:49:25.722953+00:00","model_set":{"reader":"grok-4.3"},"falsifier":"Human raters scoring the same dialogues produce trait estimates that do not match the LLM judge's scores or that erase the reported trait-specific effects of role and style.","supporting_citations":[],"review_version":1}