通过增强例句检索提升大模型对话情绪识别准确率
How to Retrieve Examples in In-context Learning to Improve Conversational Emotion Recognition using Large Language Models?
- 用改写增强方法挑选更连贯的上下文示例
- 在三个数据集上均优于随机检索，最高提升6.2%准确率
- 适合做对话情绪分析或提示工程优化的研究者
大语言模型（LLMs）已在多个领域实现广泛应用，但在主观任务如情绪识别中仍难以达到高精度。受SLT 2024 GenSER挑战赛启发，本研究探索如何在上下文学习（ICL）中检索高质量示例以提升对话情绪识别（CER）性能。提出基于随机和增强示例检索的多种策略，并分析对话上下文对识别准确率的影响。在IEMOCAP、MELD和EmoryNLP三个数据集上进行实验，结果表明：增强示例检索在所有数据集上均优于其他方法，凸显了检索连贯目标示例并经改写增强的重要性。
原文摘要 · Abstract (English)
Large language models (LLMs) have enabled a wide variety of real-world applications in various domains. However, creating a high-performing application with high accuracy remains challenging, particularly for subjective tasks like emotion recognition. Inspired by the SLT 2024 GenSER Challenge, this study investigates approaches to improving conversational emotion recognition (CER) by LLMs. Specifically, we explore how to retrieve high-quality examples in in-context learning (ICL) to enhance CER. We propose various strategies based on random and augmented example retrieval and also analyze the impact of conversational context on CER accuracy. Experiments were conducted on the three datasets including IEMOCAP, MELD and EmoryNLP. The results show that augmented example retrieval consistently outperforms other techniques under investigation across all datasets, highlighting the importance of retrieving coherent targeted examples and enhancing them through paraphrasing.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。