大型语言模型有情感吗？基于提示、检索与课程学习的情感识别教学研究

Do LLMs Feel? Teaching Emotion Recognition with Prompts, Retrieval, and Curriculum Learning

November 10, 2025

作者: Xinran Li, Xiujuan Xu, Jiaqi Qiao, Yu Liu

cs.AI

摘要

對話情緒識別（ERC）是理解人類情感並實現自然人機交互的關鍵任務。儘管大型語言模型（LLMs）近期在該領域展現出巨大潛力，但其捕捉顯性與隱性情緒內在聯繫的能力仍存在侷限。本文提出創新型ERC訓練框架PRC-Emo，融合提示工程、示範檢索與課程學習三大要素，旨在探究LLMs能否有效感知對話情境中的情緒。具體而言，我們基於顯性與隱性情緒線索設計情緒敏感型提示模板，以更精準引導模型理解說話者心理狀態；構建首個專用於ERC的示範檢索庫，包含來自廣泛使用數據集的訓練樣本，以及由LLMs生成並經人工校驗的高質量對話實例；此外，我們在LoRA微調過程中引入課程學習策略，通過融合同一說話者與不同說話者話語間的加權情緒轉移來標定對話樣本難度等級，並按由易到難的順序組織訓練。在IEMOCAP和MELD兩個基準數據集上的實驗結果表明，本方法實現了新的最優性能，證明了所提方案在增強基於LLM的情緒理解能力方面的有效性與泛化性。

English

Emotion Recognition in Conversation (ERC) is a crucial task for understanding human emotions and enabling natural human-computer interaction. Although Large Language Models (LLMs) have recently shown great potential in this field, their ability to capture the intrinsic connections between explicit and implicit emotions remains limited. We propose a novel ERC training framework, PRC-Emo, which integrates Prompt engineering, demonstration Retrieval, and Curriculum learning, with the goal of exploring whether LLMs can effectively perceive emotions in conversational contexts. Specifically, we design emotion-sensitive prompt templates based on both explicit and implicit emotional cues to better guide the model in understanding the speaker's psychological states. We construct the first dedicated demonstration retrieval repository for ERC, which includes training samples from widely used datasets, as well as high-quality dialogue examples generated by LLMs and manually verified. Moreover, we introduce a curriculum learning strategy into the LoRA fine-tuning process, incorporating weighted emotional shifts between same-speaker and different-speaker utterances to assign difficulty levels to dialogue samples, which are then organized in an easy-to-hard training sequence. Experimental results on two benchmark datasets-- IEMOCAP and MELD --show that our method achieves new state-of-the-art (SOTA) performance, demonstrating the effectiveness and generalizability of our approach in improving LLM-based emotional understanding.