mlsanityData to 7 Oct 2026

"Is the Pope Catholic?" Applying Chain-of-Thought Reasoning to Understanding Conversational Implicatures

arXiv 2305.13826 ↗First posted 23 May 2023cs.CLcited by 4

On the map: LLM reasoning → Prompting and chain-of-thought reasoning

Abstract (arXiv)

Conversational implicatures are pragmatic inferences that require listeners to deduce the intended meaning conveyed by a speaker from their explicit utterances. Although such inferential reasoning is fundamental to human communication, recent research indicates that large language models struggle to comprehend these implicatures as effectively as the average human. This paper demonstrates that by incorporating Grice's Four Maxims into the model through chain-of-thought prompting, we can significantly enhance its performance, surpassing even the average human performance on this task.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Placed in Prompting and chain-of-thought reasoning, one of 17 directions in LLM reasoning. Builds on 4 of its 9 references that are in the map. First posted on arXiv 23 May 2023.

Builds on

4 of 9 references.

References · 4

  1. 2022Training language models to follow instructions with human feedbackcited by 15,712
  2. 2023Sparks of Artificial General Intelligence: Early experiments with GPT-4cited by 2,579
  3. 2019Fine-Tuning Language Models from Human Preferencescited by 2,124
  4. 2022Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language modelscited by 1,970

Built on by

Cited by year

Citing papers per year: 2024: 2; 2025: 2.

4 papers.

  1. 2024Do Large Language Models Understand Conversational Implicature -- A case study with a chinese sitcomcited by 15
  2. 2024ChatGPT Role-play Dataset: Analysis of User Motives and Model Naturalnesscited by 7
  3. 2025They want to pretend not to understand: The Limits of Current LLMs in Interpreting Implicit Content of Political Discoursecited by 3
  4. 2025Pragmatic Theories Enhance Understanding of Implied Meanings in LLMscited by 2

Similar, not cited

Close in wording, no citation either way

  1. 2023Contrastive Chain-of-Thought Prompting
  2. 2022Chain-of-Thought Prompting Elicits Reasoning in Large Language Models
  3. 2023Symbolic Chain-of-Thought Distillation: Small Models Can Also "Think" Step-by-Step
  4. 2024Enhancing Chain of Thought Prompting in Large Language Models via Reasoning Patterns
  5. 2024Chain-of-Thought in Large Language Models: Decoding, Projection, and Activation
  6. 2025On the Same Wavelength? Evaluating Pragmatic Reasoning in Language Models across Broad Concepts
  7. 2023Boosting Language Models Reasoning with Chain-of-Knowledge Prompting
  8. 2023Eliminating Reasoning via Inferring with Planning: A New Framework to Guide LLMs' Non-linear Thinking