title: Thanos: Enhancing Conversational Agents with Skill-of-Mind-Infused Large
    Language Model
summary: To increase social bonding with interlocutors, humans naturally acquire the
  ability to respond appropriately in a given situation by considering which
  conversational skill is most suitable for the response - a process we call
  skill-of-mind. For large language model (LLM)-based conversational agents,
  planning appropriate conversational skills, as humans do, is challenging due to
  the complexity of social dialogue, especially in interactive scenarios. To
  address this, we propose a skill-of-mind-annotated conversation dataset, named
  Multifaceted Skill-of-Mind, which includes multi-turn and multifaceted
  conversational skills across various interactive scenarios (e.g., long-term,
  counseling, task-oriented), grounded in diverse social contexts (e.g.,
  demographics, persona, rules of thumb). This dataset consists of roughly 100K
  conversations. Using this dataset, we introduce a new family of
  skill-of-mind-infused LLMs, named Thanos, with model sizes of 1B, 3B, and 8B
  parameters. With extensive experiments, these models successfully demonstrate
  the skill-of-mind process and exhibit strong generalizability in inferring
  multifaceted skills across a variety of domains. Moreover, we show that Thanos
  significantly enhances the quality of responses generated by LLM-based
  conversational agents and promotes prosocial behavior in human evaluations.
paper_authors._id: 672d830975cc01b042225b23
paper_authors.name: Young-Jun Lee
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-11-15T09:31:55.077Z
paper_authors._id: 6434b6619bd5a84b5dcfa4de
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/6434b6619bd5a84b5dcfa4de/h8Q6kPNjFNc03wmdboHzq.jpeg
paper_authors.fullname: Young-Jun Lee
paper_authors.type: user
paper_authors.user: passing2961
paper_authors._id: 672d830975cc01b042225b24
paper_authors.name: Dokyong Lee
paper_authors._id: 672d830975cc01b042225b25
paper_authors.name: Junyoung Youn
paper_authors._id: 672d830975cc01b042225b26
paper_authors.name: Kyeongjin Oh
paper_authors._id: 672d830975cc01b042225b27
paper_authors.name: Ho-Jin Choi
paper_title: Thanos: Enhancing Conversational Agents with Skill-of-Mind-Infused Large
    Language Model
paper_summary: To increase social bonding with interlocutors, humans naturally acquire the
  ability to respond appropriately in a given situation by considering which
  conversational skill is most suitable for the response - a process we call
  skill-of-mind. For large language model (LLM)-based conversational agents,
  planning appropriate conversational skills, as humans do, is challenging due to
  the complexity of social dialogue, especially in interactive scenarios. To
  address this, we propose a skill-of-mind-annotated conversation dataset, named
  Multifaceted Skill-of-Mind, which includes multi-turn and multifaceted
  conversational skills across various interactive scenarios (e.g., long-term,
  counseling, task-oriented), grounded in diverse social contexts (e.g.,
  demographics, persona, rules of thumb). This dataset consists of roughly 100K
  conversations. Using this dataset, we introduce a new family of
  skill-of-mind-infused LLMs, named Thanos, with model sizes of 1B, 3B, and 8B
  parameters. With extensive experiments, these models successfully demonstrate
  the skill-of-mind process and exhibit strong generalizability in inferring
  multifaceted skills across a variety of domains. Moreover, we show that Thanos
  significantly enhances the quality of responses generated by LLM-based
  conversational agents and promotes prosocial behavior in human evaluations.
paper_ai_keywords: skill-of-mind large language model (LLM) conversational skills skill-of-mind-annotated conversation dataset Multifaceted Skill-of-Mind multi-turn conversations multifaceted conversational skills interactive scenarios social contexts skill-of-mind-infused LLMs Thanos generalizability prosocial behavior