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Large Language Models are Superpositions of All Characters: Attaining Arbitrary Role-play via Self-Alignment

Year: 2024
Type: conference-paper
Abstract: Considerable efforts have been invested in augmenting the role-playing proficiency of open-source large language models (LLMs) by emulating proprietary counterparts.Nevertheless, we posit that LLMs inherently harbor role-play capabilities, owing to the extensive knowledge of characters and potential dialogues ingrained in their vast training corpora.Thus, in this study, we introduce DITTO, a self-alignment method for role-play.DITTO capitalizes on character knowledge, encouraging an instruction-following LLM to simulate role-play dialogues as a variant of reading comprehension.This method creates a role-play training set comprising 4000 characters, surpassing the scale of currently available datasets by tenfold regarding the number of roles.Subsequently, we fine-tune the LLM using this self-generated dataset to augment its role-playing capabilities.Upon evaluating our meticulously constructed and reproducible role-play benchmark and the roleplay subset of MT-Bench, DITTO in various parameter scales consistently maintains a consistent role identity and provides accurate role-specific knowledge in multi-turn role-play conversations.Notably, it outperforms all open-source role-play baselines, showcasing performance levels comparable to advanced proprietary chatbots.Furthermore, we present the first comprehensive cross-supervision alignment experiment in the role-play domain, revealing that the intrinsic capabilities of LLMs confine the knowledge within role-play.Meanwhile, the role-play styles can be easily acquired with the guidance of smaller models.We open-source related resources in https://github.com/ OFA-Sys/Ditto.
DOI: 10.18653/v1/2024.acl-long.423
Language: English
Open Access status: hybrid
OpenAlex ID: W4402671594
FWCI: 7.633
Cites:
Cited by: 26
Related to: 10