This study asks whether a user's persona changes how a chatbot expresses and reports Big Five traits after a conversation. GPT-4o mini enacts one of a nominal set of 100 extreme tropes, actually 99 unique strings because one is duplicated, and holds a 20-turn dialogue with seven chatbots: GPT-4o, GPT-4o mini, Mistral Small 3 24B, Phi-4 14B, Llama 3.1 8B, Qwen 2.5 7B, and Gemma 2 2B. Each model has 1,000 simulations over 50 scenarios. The user receives a fixed precomputed score for its trope, while the chatbot answers a 50-item IPIP Big Five inventory before and after context. The paper correlates the user's initial score with the chatbot's claimed change and finds the most consistent positive associations for Agreeableness and Conscientiousness; Emotional Stability is weakest. It also examines explicit amplify and resist prompts, longer conversations, model size, multivariable regression, persona recognition, and a WildChat-based extension. The central descriptive evidence is genuine and relevant: when the released outputs are aggregated over the 99 unique personas, many same-trait correlations remain or become larger. The implementation nevertheless does not demonstrate an internal or persistent personality change. Every dialogue turn and questionnaire item is a fresh API call; the only continuity is the complete transcript pasted into the prompt. The chatbot pre-test is also run only once per model and the same value is subtracted from all 1,000 observations. Subtracting a constant leaves correlation unchanged, so the primary result relates repeated user profiles to transcript-conditioned post-test self-assessments. There is no memory, persistent session, weight update, transfer without the transcript, or external judge. The 1,000 rows reuse 99 user profiles and 50 scenarios, yet tests treat them as independent without persona/scenario clustering or correction across 25 correlations per model. No neutral-transcript, shuffled-speaker, user-only, chatbot-only, or blinded-rater control separates interpersonal adaptation from reading, imitation, or lexical priming. The repository publishes 12,301 text outputs and supports meaningful checking, but base outputs are incomplete for Phi-4 and Mistral; the regression table does not reproduce from public CSVs; evolution and recognition outputs are absent; and the WildChat extension releases only 300 of 600 cases without the filtered dataset, while a write-mode bug erases transcripts from output files. That extension also asks GPT-4o mini to read a conversation retrospectively rather than measuring change in the original chatbot. The faithful conclusion is that dialogue context containing synthetic user personas robustly influences subsequent psychometric responses in several LLMs. This matters for consistency, alignment, and user experience, but it does not establish that models acquire a stable personality.
Research question
To what extent does the Big Five personality of a synthetic user influence the subsequent psychometric responses of different chatbots after a prolonged conversation, which traits and models are most sensitive, and can explicit instructions, duration, scale, or real conversations modulate or confirm this effect?