灭霸：利用心智技能注入的大型語言模型增強對話代理

摘要

為了增進與對話者的社交聯繫，人類自然地獲得了在特定情況下適當回應的能力，透過考慮哪種對話技巧最適合作出回應 - 這個過程我們稱之為心智技能。對於基於大型語言模型（LLM）的對話代理人來說，像人類一樣計劃適當的對話技巧在社交對話的複雜性方面具有挑戰性，特別是在互動場景中。為了應對這一挑戰，我們提出了一個名為多面向心智技能（Multifaceted Skill-of-Mind）的對話數據集，其中包括各種互動場景（例如長期、輔導、任務導向）中的多輪和多面向對話技能，基於不同的社交背景（例如人口統計學、個人形象、經驗法則）。該數據集包含約10萬個對話。利用這個數據集，我們引入了一個新系列的注入心智技能的LLM，名為Thanos，模型規模分別為10億、30億和80億參數。通過大量實驗，這些模型成功展示了心智技能過程，並在推斷各種領域中的多面向技能方面表現出強大的泛化能力。此外，我們展示了Thanos明顯提升了基於LLM的對話代理人生成的回應質量，並在人類評估中促進了親社會行為。

English

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.

灭霸：利用心智技能注入的大型語言模型增強對話代理

Thanos: Enhancing Conversational Agents with Skill-of-Mind-Infused Large Language Model

摘要

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