Causal-ERC: A Multimodal Framework with Causal Prompting for Emotion Recognition in Conversations with Large Language Models

Ran Jing; Geng Tu; Yice Zhang; Ruifeng Xu

AAAI 2026technical0 citations

Causal-ERC: A Multimodal Framework with Causal Prompting for Emotion Recognition in Conversations with Large Language Models

Ran Jing, Geng Tu, Yice Zhang, Ruifeng Xu

Abstract

The rapid advancement of large language models (LLMs) has revitalised research in Emotion Recognition in Conversation (ERC). However, existing LLM-based ERC approaches operate solely on textual input, whereas MLLM-based emotion recognition methods in non-conversational scenarios typically perform only basic multimodal fusion and fail to consider speaker-sensitive contextual dependencies, which limits their performance on ERC tasks. To integrate multimodal cues effectively and address their limitations in handling contextual dependencies, we propose a novel LLM-based framework, Causal-ERC, which captures context representations within each modality and incorporates them into the LLM. Moreover, experimental results show that LLMs perform poorly on long conversations. To improve LLMs

BibTeX

@inproceedings{aaai2026_causalercamultim,
  title = {Causal-ERC: A Multimodal Framework with Causal Prompting for Emotion Recognition in Conversations with Large Language Models},
  author = {Ran Jing and Geng Tu and Yice Zhang and Ruifeng Xu},
  booktitle = {AAAI 2026},
  year = {2026}
}