Do LLMs Feel? Teaching Emotion Recognition with Prompts, Retrieval, and
  Curriculum Learning
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.
6912e3f3a644ba07c499c83c
Xinran Li
claimed_verified
2025-11-11T19:43:15.742Z
681227983a64768db17175ca
https://cdn-avatars.huggingface.co/v1/production/uploads/681227983a64768db17175ca/1vsh6UUgokDviy_DK4j3o.jpeg
LiXinran
LiXinran1
user
LiXinran1
6912e3f3a644ba07c499c83d
Xiujuan Xu
6912e3f3a644ba07c499c83e
Jiaqi Qiao
6912e3f3a644ba07c499c83f
Yu Liu
Do LLMs Feel? Teaching Emotion Recognition with Prompts, Retrieval, and
  Curriculum Learning
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.
A novel ERC training framework, PRC-Emo, integrates prompt engineering, demonstration retrieval, and curriculum learning to enhance LLMs' ability to perceive emotions in conversations, achieving state-of-the-art performance on benchmark datasets.
Prompt engineering demonstration retrieval curriculum learning LoRA fine-tuning emotion-sensitive prompt templates IEMOCAP MELD