# Evaluating Large language models on Understanding Korean indirect Speech acts

arXiv: https://arxiv.org/abs/2502.10995 · HTML: https://mlsanity.com/paper/2502.10995/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Feb 2025.

## Built on by (most cited first, 3 of 3)

- [Read the Room, Read the Image: Understanding Indirect Speech Acts in Multimodal Visual Contexts](https://mlsanity.com/paper/2608.30270/) (2026)
- [VakyArth: Evaluating Pragmatic Competence in LLMs across Indic Languages](https://mlsanity.com/paper/2609.01788/) (2026)
- [Lost in the Request: How Communication Variation Disrupts Retrieval and Action in Email Agents](https://mlsanity.com/paper/2610.02627/) (2026)

## Abstract (arXiv)

To accurately understand the intention of an utterance is crucial in conversational communication. As conversational artificial intelligence models are rapidly being developed and applied in various fields, it is important to evaluate the LLMs' capabilities of understanding the intentions of user's utterance. This study evaluates whether current LLMs can understand the intention of an utterance by considering the given conversational context, particularly in cases where the actual intention differs from the surface-leveled, literal intention of the sentence, i.e. indirect speech acts. Our findings reveal that Claude3-Opus outperformed the other competing models, with 71.94% in MCQ and 65% in OEQ, showing a clear advantage. In general, proprietary models exhibited relatively higher performance compared to open-source models. Nevertheless, no LLMs reached the level of human performance. Most LLMs, except for Claude3-Opus, demonstrated significantly lower performance in understanding indirect speech acts compared to direct speech acts, where the intention is explicitly revealed through the utterance. This study not only performs an overall pragmatic evaluation of each LLM's language use through the analysis of OEQ response patterns, but also emphasizes the necessity for further research to improve LLMs' understanding of indirect speech acts for more natural communication with humans.