The final ACL 2025 version of BIG5-CHAT presents a generation and training pipeline for expressing high or low levels of each Big Five trait in LLM responses. Its empirical basis is not a final human-written dialogue corpus. It starts from PsychGenerator, 846,304 Facebook posts linked to their authors' personality scores, and 10,000 SODA scenarios, themselves originally generated with GPT-3.5. The authors fine-tune five LLaMA-3-8B-Instruct expert generators, one per trait, on the upper and lower ends of PsychGenerator. They then combine expert logits with LLaMA-3-70B-Instruct through a DExperts-style method and produce 100,000 synthetic single-turn dialogues: 20,000 per trait, balanced between high and low levels. “Human-grounded” therefore refers to personality signals inherited from human social-media posts, not human authorship of BIG5-CHAT. A RoBERTa-large classifier trained on PsychGenerator reaches 93.8% on its binarized test set and assigns the intended level to 80.4% of expert-generator outputs, versus 59.2% for a GPT-4o-mini baseline. This evaluator shares the generator's source domain, and appendix experiments with Big Five Essay classifiers reach only about 50–60%, exposing weak cross-domain generalization. LLaMA-3-8B-Instruct and 70B-Instruct are aligned on BIG5-CHAT with LoRA-based SFT and DPO and compared with direct inference and instruction- or demonstration-based prompting. Across BFI-44 and IPIP-NEO-120, repeated five times at temperature 0.6, all interventions produce some high–low separation. SFT and DPO usually push scores closer to the scale extremes, especially for low-trait conditions, but DPO has no consistent overall advantage over SFT. Appendix baselines using keywords, MPI items, or LLM-written descriptions can match or exceed some fine-tuning results, although the authors exclude them from the main comparison because of lexical overlap with the questionnaires. SFT best approximates the selected human within-trait correlation matrix, with Frobenius distance 1.55 versus 2.10 for prompting and 2.06 for DPO, while notable discrepancies remain, especially for Neuroticism. Two graduate annotators compare 200 response pairs: BIG5-CHAT beats a prompt baseline on expressiveness in 50.3% of cases and realism in 47.8%, with 39.8% and 42.3% ties and kappa values of 0.50 and 0.55. The most favorable reasoning result is 70B SFT, which improves average social, mathematical, and commonsense performance, but does not uniformly beat direct inference on TruthfulQA or general knowledge. Results for 8B are far less stable: DPO collapses HumanEval in several conditions, SFT sharply reduces GSM8K, and Agreeableness and Neuroticism effects are weak or contradictory. The study therefore supports controllable output patterns under one pipeline, not a causal claim that personality improves reasoning. Training method and data remain confounded. Its hallucination discussion uses selected qualitative examples rather than a systematic metric, and the work does not test multi-trait combinations, long conversations, temporal stability, other languages, or user effects.
Research question
Can a conversational corpus based on human Big Five signals be constructed to align LLMs through SFT or DPO, to induce high and low levels more clearly than with prompting, and to relate those output profiles to reasoning task performance?