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
Reasoning-Enhanced Generation: Incorporates a structured generation method with explicit reasoning before query formulation
State-of-the-Art Performance: Achieves remarkable results across diverse retrieval tasks
Parameter Efficiency: With just 3B parameters, outperforms much larger models like GPT-4o and Claude-3.5-Sonnet

About

DeepRetrieval was developed by researchers from the UIUC CS. For more information, visit the GitHub repository.