AI RESEARCH
Implicature in Interaction: Understanding Implicature Improves Alignment in Human-LLM Interaction
arXiv CS.CL
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ArXi:2510.25426v2 Announce Type: replace 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.