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

arXiv: https://arxiv.org/abs/2305.13826 · HTML: https://mlsanity.com/paper/2305.13826/

## Key facts

- Cited by 4 papers in the map (data through 14 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.

## Placed in

- LLM reasoning / [Prompting and chain-of-thought reasoning](https://mlsanity.com/lr/prompting-and-chain-of-thought-reasoning/)

## Builds on (most cited first, 4 of 4)

- [Training language models to follow instructions with human feedback](https://mlsanity.com/paper/2203.02155/) (2022)
- [Sparks of Artificial General Intelligence: Early experiments with GPT-4](https://mlsanity.com/paper/2303.12712/) (2023)
- [Fine-Tuning Language Models from Human Preferences](https://mlsanity.com/paper/1909.08593/) (2019)
- [Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models](https://mlsanity.com/paper/2206.04615/) (2022)

## Built on by (most cited first, 4 of 4)

- [Do Large Language Models Understand Conversational Implicature -- A case study with a chinese sitcom](https://mlsanity.com/paper/2404.19509/) (2024)
- [ChatGPT Role-play Dataset: Analysis of User Motives and Model Naturalness](https://mlsanity.com/paper/2403.18121/) (2024)
- [They want to pretend not to understand: The Limits of Current LLMs in Interpreting Implicit Content of Political Discourse](https://mlsanity.com/paper/2506.06775/) (2025)
- [Pragmatic Theories Enhance Understanding of Implied Meanings in LLMs](https://mlsanity.com/paper/2510.26253/) (2025)

## 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.