| | --- |
| | license: mit |
| | --- |
| | |
| | # DeepRetrieval |
| | ## Overview |
| |
|
| | DeepRetrieval is a novel approach that uses reinforcement learning (RL) to train Large Language Models (LLMs) for query generation without requiring supervised data. Instead of relying on expensive human-annotated or distilled reference queries, DeepRetrieval enables LLMs to learn through direct trial and error, using retrieval metrics as rewards. |
| | ## Key Features |
| |
|
| | - **No Supervision Required**: Eliminates the need for expensive human-annotated or distilled reference queries |
| | - **RL-Based Framework**: Uses reinforcement learning to optimize query generation directly for retrieval performance |
| | - **State-of-the-Art Performance**: Achieves remarkable results across diverse retrieval tasks |
| |
|
| | Please view our [GitHub page](https://github.com/pat-jj/DeepRetrieval) for instructions. |
| |
|
| | [DeepRetrieval Paper](arxiv.org/abs/2503.00223) |
| | ``` |
| | @article{jiang2025deepretrievalhackingrealsearch, |
| | title={DeepRetrieval: Hacking Real Search Engines and Retrievers with Large Language Models via Reinforcement Learning}, |
| | author={Pengcheng Jiang and Jiacheng Lin and Lang Cao and Runchu Tian and SeongKu Kang and Zifeng Wang and Jimeng Sun and Jiawei Han}, |
| | year={2025}, |
| | journal = {arXiv preprint arXiv: 2503.00223}, |
| | url={https://arxiv.org/abs/2503.00223} |
| | } |
| | ``` |
