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LLM-Guided Semantic Relational Reasoning for Multimodal Intent Recognition

This repository provides the official PyTorch implementation of the research paper:

LLM-Guided Semantic Relational Reasoning for Multimodal Intent Recognition (Accepted by [EMNLP2025 Main Track]).

1.Introduction

Understanding human intents from multimodal signals is critical for analyzing human behaviors and enhancing human-machine interactions in real-world scenarios. However, existing methods exhibit limitations in their modality-level reliance, constraining relational reasoning over fine-grained semantics for complex intent understanding. This paper proposes a novel LGSRR method, which harnesses the expansive knowledge of LLMs to establish semantic foundations that boost smaller models' relational reasoning performance.

2. Dependencies

We use anaconda to create python environment and install required libraries:

cd LGSRR
conda create --name lgsrr python=3.9
conda install pytorch==1.13.1 torchvision==0.14.1 torchaudio==0.13.1 pytorch-cuda=11.7 -c pytorch -c nvidia
pip install -r requirements.txt

3. Usage

The data can be downloaded through the following links:

https://drive.google.com/drive/folders/1nCkhkz72F6ucseB73XVbqCaDG-pjhpSS

You can evaluate the performance of our proposed LGSRR on MIntRec2.0 and IEMOCAP-DA by using the following commands:

  • MIntRec2.0
sh examples/run_lgsrr_MIntRec2.0.sh
  • IEMOCAP-DA
sh examples/run_lgsrr_IEMOCAP-DA.sh

You are required to configure the path to the pre-trained model in the configs/__init__.py file.

4. Model

The overview model architecture:

LGSRR

5. Experimental Results

Experimental_Results

6. Citation

If you are insterested in this work, and want to use the codes or results in this repository, please star this repository and cite by:

@misc{zhou2025llmguidedsemanticrelationalreasoning,
      title={LLM-Guided Semantic Relational Reasoning for Multimodal Intent Recognition}, 
      author={Qianrui Zhou and Hua Xu and Yifan Wang and Xinzhi Dong and Hanlei Zhang},
      year={2025},
      eprint={2509.01337},
      archivePrefix={arXiv},
      primaryClass={cs.MM},
      url={https://arxiv.org/abs/2509.01337}, 
}

7. Acknowledgments

Some of the codes in this repo are adapted from MIntRec, and we are greatly thankful.

If you have any questions, please open issues and illustrate your problems as detailed as possible.