Think Before You Speak: Cultivating Communication Skills of Large Language Models via Inner Monologue
This work addresses the limitation of LLMs in acting as proactive, engaging chatbots for users, though it is incremental in enhancing existing models.
The authors tackled the problem of large language models lacking communication skills like topic transition and empathy, which limits their anthropomorphic appeal, and they introduced an inner monologue strategy that improved backbone models over baselines on a new benchmark.
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.