license: mit
language:
- en
base_model:
- meta-llama/Meta-Llama-3-8B-Instruct
library_name: transformers
pipeline_tag: text-generation
🧠 LLM-QE: Improving Query Expansion by Aligning Large Language Models with Ranking Preferences
This is the official model for LLM-QE: Improving Query Expansion by Aligning Large Language Models with Ranking Preferences.
The LLM-QE model is designed to enhance query expansion in information retrieval tasks by leveraging Large Language Models (LLMs), improving the alignment between LLMs and ranking preferences during query expansion.
📄 Paper
For a detailed explanation of the methodology and experiments, please refer to our paper:
LLM-QE: Improving Query Expansion by Aligning Large Language Models with Ranking Preferences
🔄 Reproduce the Results
To reproduce the experiments and benchmarks from the paper, follow the instructions provided in the official GitHub repository: 👉 GitHub: NEUIR/LLM-QE.
🛠 Model Details
- Model Name: LLM-QE-DPO
- Architecture: LLaMA3-8B-Instruct with query expansion alignment using ranking preferences
📈 Usage:
You can use this model for query expansion tasks, particularly in information retrieval systems that benefit from alignment with ranking preferences.
🔖 Citation
If you use LLM-QE in your work, please consider citing our paper:
@misc{yao2025llmqeimprovingqueryexpansion,
title={LLM-QE: Improving Query Expansion by Aligning Large Language Models with Ranking Preferences},
author={Sijia Yao and Pengcheng Huang and Zhenghao Liu and Yu Gu and Yukun Yan and Shi Yu and Ge Yu},
year={2025},
eprint={2502.17057},
archivePrefix={arXiv},
primaryClass={cs.IR},
url={https://arxiv.org/abs/2502.17057},
}