Spaces:
Running
Running
File size: 1,025 Bytes
7abe7f6 855590c 7abe7f6 50da90e 4e5b991 4f00f72 fe0dbda d5d15d0 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 |
---
license: mit
title: DeepRetrieval
emoji: π
colorTo: purple
pinned: true
short_description: Hacking Real Search Engines and Retrievers!
sdk: static
---
# 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](https://arxiv.org/pdf/2503.00223) |