让大模型理解言外之意，能显著提升人机对话的自然度和满意度。
Implicature in Interaction: Understanding Implicature Improves Alignment in Human-LLM Interaction
- 用含隐含意义的提示词测试模型推断意图能力
- 67.6%用户更偏好隐含提示生成的回复
- 小模型通过隐含提示也能大幅提升回应质量
大型语言模型（LLMs）正成为人机交互的核心。本文认为，要推进人机交互，需关注交互的语言基础，尤其是隐含意义（implicature）——即通过共享语境传达的未明说含义，这对人-智能体对齐至关重要。本研究考察了模型在上下文提示中推断用户意图的能力，以及理解隐含意义是否能改善回应质量。结果显示，大模型更接近人类的理解方式，而小模型在隐含意义推断上表现较差。此外，使用隐含意义提示可显著提升各模型回复的感知相关性与质量，尤其对小模型提升明显。总体上，67.6%参与者更偏好评价为包含隐含意义提示的回应，表明人们对情境化、微妙沟通有明确偏好。本研究展示了语言理论如何助力解决人-智能体对齐问题，使交互更自然、更贴近真实语境。
原文摘要 · Abstract (English)
The rapid advancement of Large Language Models (LLMs) is positioning language at the core of human-computer interaction (HCI). We argue that advancing HCI requires attention to the linguistic foundations of interaction, particularly implicature (meaning conveyed beyond explicit statements through shared context) which is essential for human-AI (HAI) alignment. This study examines LLMs' ability to infer user intent embedded in context-driven prompts and whether understanding implicature improves response generation. Results show that larger models approximate human interpretations more closely, while smaller models struggle with implicature inference. Furthermore, implicature-based prompts significantly enhance the perceived relevance and quality of responses across models, with notable gains in smaller models. Overall, 67.6% of participants preferred responses with implicature-embedded prompts to literal ones, highlighting a clear preference for contextually nuanced communication. Our work contributes to understanding how linguistic theory can be used to address the alignment problem by making HAI interaction more natural and contextually grounded.
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