让对话机器人主动引导用户，而非被动回答。
Redefining Proactivity for Information Seeking Dialogue
- 用新信息增强每条回复的主动性，推动对话持续。
- 构建2000条单轮对话数据集，自动评估指标与人工标注高度一致。
- 创新提示词使零样本表现提升最高达90%，适合对话系统研究者。
信息查询对话（ISD）代理旨在准确回应用户问题。尽管在直接回答方面表现良好，但这些代理及通用大模型普遍呈现被动行为，缺乏主动引导用户持续对话的能力。现有对主动性的定义未关注每条回复如何有效吸引用户并维持对话。为此，本文提出一种新定义：通过引入与初始查询相关的新信息来增强回复的主动性。我们构建了一个包含2000个单轮对话的主动对话数据集，并提出多个自动评估指标，其与人工标注具有高相关性。此外，我们设计了两种创新的思维链（CoT）提示：3步CoT与3合1CoT，其在零样本设置下性能相比标准提示最高提升90%。
原文摘要 · Abstract (English)
Information-Seeking Dialogue (ISD) agents aim to provide accurate responses to user queries. While proficient in directly addressing user queries, these agents, as well as LLMs in general, predominantly exhibit reactive behavior, lacking the ability to generate proactive responses that actively engage users in sustained conversations. However, existing definitions of proactive dialogue in this context do not focus on how each response actively engages the user and sustains the conversation. Hence, we present a new definition of proactivity that focuses on enhancing the `proactiveness' of each generated response via the introduction of new information related to the initial query. To this end, we construct a proactive dialogue dataset comprising 2,000 single-turn conversations, and introduce several automatic metrics to evaluate response `proactiveness' which achieved high correlation with human annotation. Additionally, we introduce two innovative Chain-of-Thought (CoT) prompts, the 3-step CoT and the 3-in-1 CoT prompts, which consistently outperform standard prompts by up to 90% in the zero-shot setting.
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