Prompting and Evaluating Large Language Models for Proactive Dialogues: Clarification, Target-guided, and Non-collaboration (2023)

Abstract: Conversational systems based on Large Language Models (LLMs), such as ChatGPT, show exceptional proficiency in context understanding and response generation. However, they still possess limitations, such as failing to ask clarifying questions to ambiguous queries or refuse users’ unreasonable requests, both of which are considered as key aspects of a conversational agent’s proactivity. This raises the question of whether LLM-based conversational systems are equipped to handle proactive dialogue problems. In this work, we conduct a comprehensive analysis of LLM-based conversational systems, specifically focusing on three key aspects of proactive dialogues: clarification, target-guided, and non-collaborative dialogues. To trigger the proactivity of LLMs, we propose the Proactive Chain-of-Thought prompting scheme, which augments LLMs with the goal planning capability over descriptive reasoning chains. Empirical findings are discussed to promote future studies on LLM-based proactive dialogue systems.

## introduction conversational systems are envisioned to provide social support or functional service to human users via natural language interactions. most research typically centers around a system's response capabilities, such as understanding the dialogue context @xcite @xcite and generating appropriate responses @xcite . the popularity of conversational systems has grown unprecedentedly with the advent of chat-gpt, which showcases exceptional capabilities of context understanding and response generation with large language models (llms). recent studies observe that, compared with current fine-tuned state-of-the-art (sota) methods, chatgpt can still achieve competitive performance under zero-shot setting on different dialogue problems, such as the knowledge-grounded dialogues @xcite , task-oriented dialogues @xcite , and emotion-aware dialogues @xcite . despite the strength of chatgpt, there are still several limitations foot_0 , such as failing to ask clarification questions to ambiguous user queries or refuse problematic user requests. these kinds of capabilities are typically regarded as the proactivity of the conversational system @xcite , where the system can create or control the conversation to achieve the conversational goals by taking initiative and anticipating impacts on themselves or the human users. thus, it raises the question: are these llm-based conversational systems equipped to manage proactive dialogue problems? in this work, we conduct the first comprehensive analysis of llm-based conversational systems on three common aspects of proactive dialogues, including 1) clarification in information-seeking dialogues @xcite where the system is required to proactively ask clarification questions when encountering ambiguity in user queries; 2) target-guided open-domain dialogues @xcite where the system is required to proactively lead the conversation towards the designated target; and 3) non-collaborative task-oriented dialogues @xcite @xcite where the system and the user do not share the same conversational goal while the system aims to strategically reach a consensus with the user. motivated by the emergent capabilities of llms @xcite on reasoning over texts, some recent studies investigate in-context learning or chain-of-thought prompting schemes on planning @xcite or taking actions @xcite in interactive environments. similarly, strategy learning and goal planning attach great importance in proactive dialogue systems. in order to enhance the proactivity of llm-based conversa-task background: the grounded document is "angie …. she made a drawing of her mother. her mother found a large red book. then they went to the mystery section. angie sat in a blue chair. she drew a picture of her brother. her mother found the book. it was a green book. …" conversation history: ["user": "what did she draw?", "system": "her mother", "user": "what did her mother find?", "system": "the book", "user": "what color was it?"] (1) clarification dialogues: abg-coqa (1a) standard (1b) proactive (1c) proactive cot strategy: ["build rapport", "show dominance", "negative sentiment", …] act: ["ask a question", "insist on an offer", "proposing a counter price", …] prompt: given the task background and the conversation history, first analyse the current negotiation progress and consider an appropriate negotiation goal, and then select appropriate strategies and actions to generate the response: response: the seller firmly rejects the price. it would be better to propose a higher price closed to 8700. the strategy is negative sentiment. the act is proposing a counter price. i really don't need it but i can pay you 8200 if you let it go. strategy: ["build rapport", "show dominance", …] act: ["ask a question", "insist on an offer", …] prompt: given the task background and the conversation history, please use appropriate strategies and actions to generate the response: response: the strategy is communicate politely. the act is proposing a counter price. i understand. how about we meet halfway at 10250? what do you think? prompt: given the task background and the conversation history, please generate the response: response: how about we meet in the middle at 10000? tional systems, we design the proactive chain-ofthought prompting (procot) scheme. as shown in figure 1 , with standard prompting, llm-based systems directly provide a randomly-guessed answer to the ambiguous user question (1a), or generate a general bargain response without any negotiation strategy (2a). when providing the system with options to take different dialogue acts (proactive prompting), the generated responses are unaware of the conversational goal, such as generating underspecified clarification questions (1b) and conservative negotiation responses (2b). to this end, pro-cot first instructs the system to generate descriptive thoughts about intermediate steps of reasoning and planning for reaching the conversational goal, and then make the decision of the next action to take. finally, the system generates an appropriate response based on the decided action (1c & 2c). we conduct extensive experiments with two llm-based conversational systems, including chatgpt and an open-sourced model, vicuna @xcite . with the aforementioned three types of prompting schemes, we compare these llm-based conversational systems with fine-tuned sota dialogue models. the main contributions of this work can be summarized as follows: large language models for dialogues. previous dialogue systems, such as dialogpt @xcite , meena @xcite , blenderbot @xcite , lamda @xcite , typically fine-tune pre-trained language models on public dialogue data. inspired by the success of chatgpt, recent practices build dialogue systems through conducting supervised fine-tuning on open-source large language models, such as llama @xcite , with either constructed instruction-following examples (e.g., alpaca @xcite ) or distilled conversation data (e.g., vicuna @xcite ) from chat-gpt. as all these llm-based dialogue systems are trained to follow the user's instruction, it remains a question on whether these systems can take the initiative for handling proactive dialogues. prompting in dialogue systems. to induce knowledge from llms, various prompting methods are designed for zero-shot or few-shot learning in dialogue applications, such as task-oriented dialogues @xcite , knowledge-grounded dialogues @xcite @xcite , and open-domain dialogues @xcite @xcite . for example, @xcite propose to prompt llms for controllable response generation in emotional support and persuasion dialogues, conditioned on the ground-truth dialogue strategies. in this work, we aim at prompting llms to proactively interact with the users. ## prompting llms to be proactive as presented in figure 1 , we describe the prompting schemes, including the standard, proactive, and proactive chain-of-thought (procot) prompting. standard prompting. in order to instruct llms to perform specific dialogue tasks, the typical prompting scheme can be formulated as p(r|d, c). given the task background d and the conversation history c, instruct the llm to generate the response r. in specific, the task background can be the grounded document in clarification dialogues or the target description in target-guided dialogues. proactive prompting. proactive prompting aims to provide alternative options for llms to decide what kinds of actions should be taken in the response, instead of simply responding to the instruction. it can be formulated as: p(a, r|d, c, a). given the task background d, the conversation history c, and a set of possible dialogue acts a, instruct the llm to select the most appropriate dialogue act @xmath0. for example, the dialogue act can be ask a clarification question or directly answer the question in clarification dialogues, different negotiation strategies in non-collaborative dialogues, or different conversation topics in target-guided dialogues. proactive chain-of-thought prompting. in order to endow llms with the capability of planning and taking the initiative towards the ultimate goal, we develop the proactive chain-of-thought prompting scheme-procot. it involves the analysis of the next action to take by performing dynamic reasoning and planning for reaching the conversational goal. procot can be formulated as: ## evaluation we evaluate the proactivity of llm-based conversational systems from three perspectives, including the capability of asking clarification questions ( § 4.1), guiding the conversation towards the designated target ( § 4.2), and strategically handling conflicting goals ( § 4.3). ## conclusion in this work, we conduct the first comprehensive evaluation on the capability of llm-based dialogue systems in handling proactive dialogues, including clarification, target-guided, and noncollaborative dialogues. to enhance the proactivity of llm-based dialogue systems, we propose a proactive chain-of-thought prompting scheme that triggers the reasoning and planning capability of llms. the empirical analysis sheds light on the potentials of llms for proactive dialogues: 1) pro-cot largely enhances the originally poor performance of llms in asking clarification questions, but still limits in handling domain-specific applications. 2) llm-based dialogue systems perform aggressive topic shifting towards the designated target, while procot enables the topic planning to be smoother. 3) despite the strength on controllable response generation, the capability of strategy learning and planning is a key challenge for llms in handling non-collaborative dialogues. problem. in fact, prompt sensitivity and optimality themselves are valuable research problems in dialogue systems, which can be further investigated in the future studies. to facilitate the reproducibility of this work, we will release all the prompts used in the experiments and provide detailed descriptions about the designs of each prompting scheme in appendix c. the code and data will be released via https://github.com/dengyang17/ llm-proactive . financial and computational cost of llms it is financially expensive to call the api of commercial llms for experiments. in our experiments, it costs about $120 to call the openai api for getting all the experimental results of chatgpt. on the other hand, it is computationally expensive to conduct experiments with open-source llms in local machines. in our experiments, we choose vicuna 13b as the open-source llm for evaluation, which can be adapted to nvidia dgx-1 v100 32g for inference. if more budgets and better experimental environment are permitted, it would be great to evaluate how other larger llms performs in the concerned proactive dialogue problems, such as gpt-4, llama/vicuna 65b, etc.