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 a crucial ability: communication skills. This limitation renders them more like information seeking tools rather than anthropomorphic chatbots. Communication skills, such as topic transition, proactively asking questions, concept guidance, empathy, and summarising often should be taken into consideration, to make LLMs more anthropomorphic and proactive during the conversation, thereby increasing the interest of users and attracting them to chat for longer. However, enabling these communication skills in black-box LLMs remains a key challenge because they do not have the same utterance formation mode as real people: think before speaking. Inspired by linguistics and cognitive science, we empower LLMs with communication skills through inner monologues. To evaluate various communication skills, we construct a benchmark named Cskills, 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.

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、连贯且多样化的回复。然而，LLMs 仍缺乏一项关键能力：沟通技巧。这一局限使其更像信息检索工具，而非拟人化的聊天机器人。为增强对话中的拟人化程度与主动性，从而提升用户兴趣并吸引更长时间互动，需考虑话题转换、主动提问、概念引导、共情与总结等沟通技巧。然而，在黑箱LLMs中实现这些技巧仍是一项关键挑战，因其不具备真实人类的言语生成模式——在说话前先思考。受语言学与认知科学启发，我们通过内心独白赋予LLMs沟通技巧。为评估多种沟通技巧，我们构建了名为Cskills的基准测试，该测试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策略改善了骨干模型，且优于基线方法。