title: Large Language Models are Superpositions of All Characters: Attaining
    Arbitrary Role-play via Self-Alignment
summary: 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.
paper_authors._id: 65b07473afe15cbbf59d3990
paper_authors.name: Keming Lu
paper_authors.status: admin_assigned
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paper_authors.fullname: Keming Lu
paper_authors.type: user
paper_authors.user: keminglu
paper_authors._id: 65b07473afe15cbbf59d3991
paper_authors.name: Bowen Yu
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-01-24T09:12:41.920Z
paper_authors._id: 63d9d68c1cae35c27bf7a6a7
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paper_authors.fullname: Bowen Yu
paper_authors.type: user
paper_authors.user: Tigerph
paper_authors._id: 65b07473afe15cbbf59d3992
paper_authors.name: Chang Zhou
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-01-24T09:13:01.471Z
paper_authors._id: 622892774323cef93a956a4a
paper_authors.avatarUrl: /avatars/e57ef5c3b0c4289988ccd42f14e54336.svg
paper_authors.fullname: chang zhou
paper_authors.type: user
paper_authors.user: jiemizc
paper_authors._id: 65b07473afe15cbbf59d3993
paper_authors.name: Jingren Zhou
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-01-24T09:13:12.481Z
paper_authors._id: 602f88f5e8149a962412a667
paper_authors.avatarUrl: /avatars/b78f0e583df8e5d5e3365934fe5f4900.svg
paper_authors.fullname: Zhou
paper_authors.type: user
paper_authors.user: Jingren
paper_title: Large Language Models are Superpositions of All Characters: Attaining
    Arbitrary Role-play via Self-Alignment
paper_summary: 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.
paper_ai_summary: 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.
paper_ai_keywords: 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