# Pragmatic Metacognitive Prompting Improves LLM Performance on Sarcasm Detection

arXiv: https://arxiv.org/abs/2412.04509 · HTML: https://mlsanity.com/paper/2412.04509/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 28 references that are in the map.
- First posted on arXiv 4 Dec 2024.

## Builds on (most cited first, 5 of 5)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [LLaMA: Open and Efficient Foundation Language Models](https://mlsanity.com/paper/2302.13971/) (2023)
- [Reflexion: Language Agents with Verbal Reinforcement Learning](https://mlsanity.com/paper/2303.11366/) (2023)
- [Tensor Fusion Network for Multimodal Sentiment Analysis](https://mlsanity.com/paper/1707.07250/) (2017)
- [Sentiment Analysis in the Era of Large Language Models: A Reality Check](https://mlsanity.com/paper/2305.15005/) (2023)

## Built on by (most cited first, 19 of 19)

- [IMPersona: Evaluating Individual Level LM Impersonation](https://mlsanity.com/paper/2504.04332/) (2025)
- [Large Language Models for Subjective Language Understanding: A Survey](https://mlsanity.com/paper/2508.07959/) (2025)
- [What Makes a Good Natural Language Prompt?](https://mlsanity.com/paper/2506.06950/) (2025)
- [MUStReason: A Benchmark for Diagnosing Pragmatic Reasoning in Video-LMs for Multimodal Sarcasm Detection](https://mlsanity.com/paper/2510.23727/) (2025)
- [Seeing Sarcasm Through Different Eyes: Analyzing Multimodal Sarcasm Perception in Large Vision-Language Models](https://mlsanity.com/paper/2503.12149/) (2025)
- [IRONIC: Coherence-Aware Reasoning Chains for Multi-Modal Sarcasm Detection](https://mlsanity.com/paper/2505.16258/) (2025)
- [Rhetorical Questions in LLM Representations: A Linear Probing Study](https://mlsanity.com/paper/2604.14128/) (2026)
- [Context-Aware Pragmatic Metacognitive Prompting for Sarcasm Detection](https://mlsanity.com/paper/2511.21066/) (2025)
- [Sarc7: Evaluating Sarcasm Detection and Generation with Seven Types and Emotion-Informed Techniques](https://mlsanity.com/paper/2506.00658/) (2025)
- [Unveiling the Limits of Large Language Models in Inferring Pragmatic Meaning from Non-Verbal Responses](https://mlsanity.com/paper/2606.01845/) (2026)
- [Metacognition in LLMs: Foundations, Progress, and Opportunities](https://mlsanity.com/paper/2607.11881/) (2026)
- [Nek Minit: Harnessing Pragmatic Metacognitive Prompting for Explainable Sarcasm Detection of Australian and Indian English](https://mlsanity.com/paper/2505.15095/) (2025)
- [Yor-Sarc: A gold-standard dataset for sarcasm detection in a low-resource African language](https://mlsanity.com/paper/2602.18964/) (2026)
- [DRInQ: Evaluating Conversational Implicature with Controlled Context Variation](https://mlsanity.com/paper/2605.24267/) (2026)
- [Re-defining Humor Data Objects for AI Humor Research](https://mlsanity.com/paper/2605.25171/) (2026)
- [The Register Gap: A Meaning Intelligence Framework for Nigerian Public Discourse](https://mlsanity.com/paper/2606.20255/) (2026)
- [Towards High-Level Semantic Intelligence](https://mlsanity.com/paper/2607.24082/) (2026)
- [Figurative and Cultural Knowledge in LLMs: Investigating Cross-Domain Transfer through Fine-Tuning](https://mlsanity.com/paper/2608.18361/) (2026)
- [Emo-Jev: Probabilistic Reasoning for Emotion Classification with Jev](https://mlsanity.com/paper/2610.08829/) (2026)

## Abstract (arXiv)

Sarcasm detection is a significant challenge in sentiment analysis due to the nuanced and context-dependent nature of verbiage. We introduce Pragmatic Metacognitive Prompting (PMP) to improve the performance of Large Language Models (LLMs) in sarcasm detection, which leverages principles from pragmatics and reflection helping LLMs interpret implied meanings, consider contextual cues, and reflect on discrepancies to identify sarcasm. Using state-of-the-art LLMs such as LLaMA-3-8B, GPT-4o, and Claude 3.5 Sonnet, PMP achieves state-of-the-art performance on GPT-4o on MUStARD and SemEval2018. This study demonstrates that integrating pragmatic reasoning and metacognitive strategies into prompting significantly enhances LLMs' ability to detect sarcasm, offering a promising direction for future research in sentiment analysis.