The emergence of large language models (LLMs) further improves the capabilities of open-domain dialogue systems and can generate fluent, coherent, and diverse responses. However, LLMs still lack an important ability: communication skills, which makes them more like information seeking tools than anthropomorphic chatbots. To make LLMs more anthropomorphic and proactive during the conversation, we add five communication skills to the response generation process: topic transition, proactively asking questions, concept guidance, empathy, and summarising often. The addition of communication skills increases the interest of users in the conversation and attracts them to chat for longer. To enable LLMs better understand and use communication skills, we design and add the inner monologue to LLMs. The complete process is achieved through prompt engineering and in-context learning. To evaluate communication skills, we construct a benchmark named Cskills for evaluating various communication skills, which can also more comprehensively evaluate the dialogue generation ability of the model. Experimental results show that the proposed CSIM strategy improves the backbone models and outperforms the baselines in both automatic and human evaluations.

翻译：大语言模型（LLMs）的出现进一步提升了开放域对话系统的能力，能够生成流畅、连贯且多样的回复。然而，LLMs仍缺乏一项重要能力：沟通技能，这使得它们更像信息检索工具，而非拟人化的聊天机器人。为使LLMs在对话过程中更具拟人化和主动性，我们在响应生成过程中增加了五项沟通技能：话题转换、主动提问、概念引导、共情以及经常性总结。沟通技能的加入提升了用户在对话中的兴趣，并吸引他们进行更长时间的交流。为使LLMs更好地理解并运用沟通技能，我们设计并引入了LLMs的内心独白。整个过程通过提示工程和情境学习实现。为评估沟通技能，我们构建了一个名为Cskills的基准测试集，用于评估各类沟通技能，该基准测试集也能更全面地评估模型的对话生成能力。实验结果表明，所提出的CSIM策略增强了主干模型，并在自动评估和人工评估中均优于基线方法。