RAG-Star: Enhancing Deliberative Reasoning with Retrieval Augmented Verification and Refinement
This addresses the challenge of enhancing reasoning capabilities in LLMs for complex tasks, representing an incremental advancement over existing RAG and reasoning methods.
The paper tackles the problem of LLMs struggling with complex reasoning tasks by proposing RAG-Star, a novel RAG approach that integrates retrieved information to guide tree-based deliberative reasoning, resulting in significant performance improvements over previous methods as demonstrated with models like Llama-3.1-8B-Instruct and GPT-4o.
Existing large language models (LLMs) show exceptional problem-solving capabilities but might struggle with complex reasoning tasks. Despite the successes of chain-of-thought and tree-based search methods, they mainly depend on the internal knowledge of LLMs to search over intermediate reasoning steps, limited to dealing with simple tasks involving fewer reasoning steps. In this paper, we propose \textbf{RAG-Star}, a novel RAG approach that integrates the retrieved information to guide the tree-based deliberative reasoning process that relies on the inherent knowledge of LLMs. By leveraging Monte Carlo Tree Search, RAG-Star iteratively plans intermediate sub-queries and answers for reasoning based on the LLM itself. To consolidate internal and external knowledge, we propose an retrieval-augmented verification that utilizes query- and answer-aware reward modeling to provide feedback for the inherent reasoning of LLMs. Our experiments involving Llama-3.1-8B-Instruct and GPT-4o demonstrate that RAG-Star significantly outperforms previous RAG and reasoning methods.