Thanos: Enhancing Conversational Agents with Skill-of-Mind-Infused Large
  Language Model
Authors: name: Young-Jun Lee, name: Dokyong Lee, name: Junyoung Youn, name: Kyeongjin Oh, name: Ho-Jin Choi

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.