Think Before You Speak: Cultivating Communication Skills of Large Language Models via Inner Monologue (2024)

Abstract: 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.

## introduction open-domain dialogue systems need to generate fluent, coherent, and diverse responses based on history utterances. the emergence of large language models @xcite @xcite further enhances the capabilities of dialogue generation systems and can meet the above requirements. however, llms are more like an information seeking tool than a chatbot like a real person. such a dialogue system may make users lose interest in chatting and terminate the conversation. the reason is that llms still lack an important conversational ability: communication skills. as shown in figure 1 , llm makes recommendations without a thorough comprehension of the preferences of the user regarding movie genres. this lack of detailed understanding may result in inaccurate recommendation outcomes. people use proactively asking questions in communication skills to further understand the needs of the user, thereby making better recommendations. in linguistics, communication skills are used to enhance the interactive experience during the conversation and to establish effective communication @xcite @xcite . the five common communication skills are topic transition, proactively asking questions, concept guidance, empathy, and summarising often. each communication skill is applicable to different conversational situations and plays a different role during the conversation. by using topic transition @xcite , we can avoid unfamiliar concepts and transition to familiar ones, leading to better conversations. proactively ask-ing questions @xcite can help us clarify ambiguous information and make appropriate responses based on it. concept guidance @xcite can strengthen the connection of concepts in a conversation and increase the proactivity of the dialogue. empathy @xcite can produce more personal and informative responses, increasing the interest of the speaker in chatting. summarising often @xcite allows speakers to confirm whether a consensus has been reached on the previous information, reducing the occurrence of misunderstandings. introducing communication skills to llms is not easy because they do not have the same utterance formation mode as real people: think before speaking. that is, llms do not have the same thinking process as real people before generating responses. llms are black-boxes makes understanding their decision-making process more challenging. existing works in psychology and cognitive science indicate that humans think before speaking when they have a conversation @xcite @xcite . @xcite proposes conversations can be decomposed into four segments: listening, thinking, speaking, and waiting, which also illustrates the importance of thinking before speaking. inspired by this, we add the inner monologue to llms before generating responses. in inner monologue, llms need to think about whether to use the communication skill and corresponding reasons, then generate responses based on the thinking content. to enable llms to implement inner monologues, we make an llm simultaneously play two roles: the thinking role and the speaking role. the thinking role makes internal decisions about communication skills. the speaking role generates responses and chats with users. we technically use prompt engineering @xcite and incontext learning (icl) to achieve the above process. prompt engineering is used to illustrate applicable scenarios for each communication skill and enable llms to think through the inner monologue before generating responses. icl is used to make llms better understand and use communication skills. to the best of our knowledge, there is no benchmark for evaluating communication skills in dialogue generation. in order to evaluate the effect of dialogue generation after adding communication skills, we constructed evaluation data for each communication skill to form a benchmark, named cskills. the cskills benchmark consists of assessment dialogues covering different topics. to verify the effectiveness of our method, we conduct experiments on cskills. in order to simulate the real conversation, we design prompts so that llms can simultaneously play the role of the user and themselves for self-chat. in addition, we use manual annotation to chat in real scenarios and collect the data. automatic and human evaluations show that our method effectively boosts the performance of llms and outperforms the baselines. our contributions to this paper are three folds: ## communication skills in linguistics, communication skills are used to establish effective communication and increase the satisfaction of speakers during the conversation. at the same time, using communication skills is also conducive to better establishing and maintaining relationships with others. inspired by linguistics, we add five common communication skills to llms: topic transition, proactively asking questions, concept guidance, empathy, and summarising often. ## inner monologue in psychology and cognitive science, the pauses during speaking are related to the thinking pro- ## cskills benchmark to the best of our knowledge, there is no benchmark for evaluating communication skills in dialogue generation. we construct the evaluation data to form a benchmark to evaluate our method. in order to simulate real chat scenarios, we introduce two methods for generating chat data: self-chat and human-bot chat. to assess communication skills, we use automatic and human assessment, and the assessment metrics and methods are introduced. ## experiments to verify the effectiveness of csim, experiments are conducted on cskills. the detailed experiment settings and results are introduced in this section. ## related work in related work, open-domain dialogue generation and prompt engineering are introduced. ## conclusion in this work, we propose a simple but effective strategy to improve the anthropomorphism and proactivity of llms. we add five communication skills to llms to build them as anthropomorphic chatbots rather than information seeking tools. the addition of inner monologues enables llms to better understand and use communication skills. meanwhile, we construct a benchmark to evaluate them. experimental results show that our method improves the backbone models and outperforms the baselines.