Spaces:
Running
Running
| # 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. |