Pragmatic Theories as Prompts Improve Language Models' Understanding of Implied Meanings
Researchers found that providing large language models with overviews of pragmatic theories — such as Gricean pragmatics and Relevance Theory — as prompts significantly improves their ability to interpret implied meanings. The approach outperformed a standard 0-shot Chain-of-Thought baseline by up to 9.6% on pragmatic reasoning tasks. This matters because interpreting implied meaning is fundamental to human communication, and closing this gap in LLMs could improve their practical usefulness in nuanced language tasks.
A study posted to arXiv proposes an in-context learning method in which language models are given structured overviews of established pragmatic theories — including Gricean pragmatics and Relevance Theory — as part of their prompts, guiding them through step-by-step reasoning to arrive at interpretations of implied meaning. Compared to a 0-shot Chain-of-Thought baseline that prompts intermediate reasoning without any theoretical framework, the new approach achieved up to 9.6% higher scores on pragmatic reasoning benchmarks. The researchers also found that even simply naming a pragmatic theory in the prompt, without explaining it in detail, produced modest performance gains of around 1–3% in larger models. This suggests that larger models may have internalized some knowledge of these theories during pretraining, allowing name-only references to activate relevant reasoning patterns. The findings highlight a promising and low-cost strategy for improving LLM performance on tasks requiring nuanced language understanding.
What's missing
It is unclear whether the gains hold across diverse languages and cultural contexts, since pragmatic norms vary significantly across languages.
What different sources said
- arXiv cs.CLCenter
Pragmatic Theories Enhance Understanding of Implied Meanings in LLMs
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