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.

通过设计和添加内心独白，通过提示工程和上下文学习的方式，使大型语言模型（LLMs）更具人类形象和积极主动的对话能力，并构建评估模型对话生成能力的综合性基准 CSkills，并实验证明了所提出的 CSIM 策略在自动评估和人工评估上优于基准模型。