Implicature in Interaction: Understanding Implicature Improves Alignment in Human-LLM Interaction
This addresses the alignment problem in human-AI interaction by making communication more natural and contextually grounded, though it is incremental as it applies existing linguistic theory to LLMs.
The study tackled the problem of improving human-LLM alignment by leveraging implicature (context-driven meaning) in prompts, finding that larger models better approximate human interpretations and implicature-based prompts enhance response quality, with 67.6% of participants preferring them over literal prompts.
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.