Large Language Models are Superpositions of All Characters: Attaining
  Arbitrary Role-play via Self-Alignment
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 4,000 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 at https://github.com/OFA-Sys/Ditto.
65b07473afe15cbbf59d3990
Keming Lu
admin_assigned
2024-01-24T09:12:08.850Z
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Keming Lu
user
keminglu
65b07473afe15cbbf59d3991
Bowen Yu
admin_assigned
2024-01-24T09:12:41.920Z
63d9d68c1cae35c27bf7a6a7
/avatars/b5ad98cf269ae5f1fe90861fb4170fae.svg
Bowen Yu
user
Tigerph
65b07473afe15cbbf59d3992
Chang Zhou
admin_assigned
2024-01-24T09:13:01.471Z
622892774323cef93a956a4a
/avatars/e57ef5c3b0c4289988ccd42f14e54336.svg
chang zhou
user
jiemizc
65b07473afe15cbbf59d3993
Jingren Zhou
admin_assigned
2024-01-24T09:13:12.481Z
602f88f5e8149a962412a667
/avatars/b78f0e583df8e5d5e3365934fe5f4900.svg
Zhou
user
Jingren
Large Language Models are Superpositions of All Characters: Attaining
  Arbitrary Role-play via Self-Alignment
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 4,000 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 at https://github.com/OFA-Sys/Ditto.
Ditto, a self-alignment method, enhances the role-playing capabilities of open-source large language models by fine-tuning them on a large generated dataset, achieving performance comparable to proprietary chatbots.
role-playing proficiency large language models (LLMs) self-alignment method character knowledge reading comprehension role-play training set fine-tuning role-play benchmark roleplay subset of MT-Bench multi-turn role-play conversations cross-supervision alignment experiment