---
license: mit
language:
- en
base_model:
- facebook/contriever
library_name: sentence-transformers
pipeline_tag: feature-extraction
---


# 🧠 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](https://arxiv.org/abs/2502.17057)**.  

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**](https://arxiv.org/abs/2502.17057)

---

## 🔄 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](https://github.com/NEUIR/LLM-QE).

## 🛠 Model Details
- Model Name: LLM-QE-Contriever
- Architecture: Contriever Model with supervised contrastive learning training using the query expansions

## 📈 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}, 
}

```