  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.
