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 for instructions.

DeepRetrieval Paper