Thanos: Enhancing Conversational Agents with Skill-of-Mind-Infused Large
  Language Model
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.
672d830975cc01b042225b23
Young-Jun Lee
claimed_verified
2024-11-15T09:31:55.077Z
6434b6619bd5a84b5dcfa4de
https://cdn-avatars.huggingface.co/v1/production/uploads/6434b6619bd5a84b5dcfa4de/h8Q6kPNjFNc03wmdboHzq.jpeg
Young-Jun Lee
user
passing2961
672d830975cc01b042225b24
Dokyong Lee
672d830975cc01b042225b25
Junyoung Youn
672d830975cc01b042225b26
Kyeongjin Oh
672d830975cc01b042225b27
Ho-Jin Choi
Thanos: Enhancing Conversational Agents with Skill-of-Mind-Infused Large
  Language Model
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.
A new dataset and family of large language models, Thanos, improve conversational skills and quality of responses in social dialogue by infusing multifaceted conversational skills.
skill-of-mind conversational agents skill-of-mind-annotated Multifaceted Skill-of-Mind multifaceted conversational skills parameter-efficient large language model generalizability prosocial behavior