Ditto asks an LLM to generate its own role-play supervision. For each character it collects Wikidata attributes and a Wikipedia profile, pairs characters to produce relevant and incompatible questions, gives the full profile to the same model for response simulation, and finally fine-tunes it with only a short introduction rather than the detailed profile. The paper applies this process to Qwen-Chat at 1.8B, 7B, 14B and 72B. WikiRole is reported as 3,902 roles, 7,086 English/Chinese sessions and 36,164 turns; the test holds out 100 characters, 53 English and 47 Chinese, not used for training, with 498 turns generated by GPT-4. GPT-4-1106-preview judges three dimensions: identifying the character among four candidates, scoring evidence-supported knowledge from 1 to 10, and classifying whether the model rejects a question outside the character's era or scope. Ditto improves all three reported figures at every size. Qwen-72B moves from 0.54/4.92/0.67 to 0.90/6.64/0.82 for identity/knowledge/rejection and from 8.13 to 8.43 on the MT-Bench role-play subset. Qwen-Max nevertheless scores 0.92/8.33/0.91, and GPT-4 exceeds Ditto-72B on knowledge and rejection. The 4 x 4 cross-supervision experiment suggests that role-play format or style can be learned even from a smaller supervisor, while knowledge depends more on the seed model. This is plausible but descriptive: there are no intervals, tests or seed replications. Artifact auditing reveals important limits. Identity is judged one turn at a time and the judge also sees the user question; an answer-blind lexical baseline selects the correct candidate for 249/498 turns (50%, versus 25% chance), so the metric does not isolate style or multi-turn consistency. Only 97/498 questions require rejection, meaning never rejecting already yields 80.52% accuracy. Ditto-72B does achieve 92.78% recall on those unknown questions, but falsely rejects 85 of 401 answerable questions. The repository publishes the test, one Ditto-72B response set and 25 score files, but not the training corpus, simulation/fine-tuning code or checkpoints. Several outputs omit or duplicate roles because evaluator failures can disappear silently. Overall, the paper provides useful evidence of improved Qwen role-play and an interesting benchmark, but it does not show that LLMs are literally superpositions of characters, that identity is stable, or that Ditto achieves arbitrary and safe role-play.
Research question
Can an LLM improve its role-play by generating conversations from profiles it already knows how to read and then fine-tuning with them, and what does cross-supervision reveal about style transfer versus the limits of the base model's knowledge?