Pragmatic Theories as Prompts Improve Language Models' Understanding of Implied Meanings
Two recent studies examine how language models process pragmatic meaning and develop consistent personas through dialogue. The first demonstrates that providing pragmatic theories as prompts improves LLM performance on implied meaning tasks by up to 9.6%, while the second proposes using bridging inference to discover stable LLM personas at the discourse level rather than surface features. These findings advance understanding of how LLMs handle nuanced language comprehension and maintain coherent communication patterns.
Two complementary arXiv papers investigate different aspects of language model sophistication. The first study shows that in-context learning with pragmatic theories—including Gricean pragmatics and Relevance Theory—significantly enhances LLM performance on tasks requiring interpretation of implied meanings, with improvements of up to 9.6% over baseline Chain-of-Thought prompting. Notably, merely mentioning pragmatic theory names yields 1-3% gains in larger models. The second paper proposes a novel framework using bridging inference and knowledge graphs to discover LLM personas by analyzing discourse-level coherence rather than surface-level lexical patterns. Testing across models ranging from small-scale to 80-billion-parameter systems, the approach demonstrates that persona traits are consistently encoded in discourse structure. Together, these studies suggest that LLMs possess deeper linguistic capabilities than previously understood through surface-level analysis.
What's missing
Both papers lack discussion of potential limitations in their experimental designs, such as the size and diversity of test datasets, generalization to non-English languages, or whether improvements persist across different model architectures and training paradigms. The first study does not specify which pragmatic reasoning tasks were evaluated or provide error analysis. The second study does not discuss computational costs of constructing bridging-inference graphs or compare against other discourse-aware persona discovery methods.
What different sources said
- arXiv cs.CLCenter
Pragmatic Theories Enhance Understanding of Implied Meanings in LLMs
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