RAG-Star: Enhancing Deliberative Reasoning with Retrieval Augmented Verification and Refinement (2412.12881v1)

Abstract: Existing 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.

RAG-Star: Enhancing Deliberative Reasoning with Retrieval Augmented Verification and Refinement

The paper presents a novel approach entitled RAG-Star, which is designed to improve the performance of LLMs on complex reasoning tasks that involve multiple steps, such as multi-hop question answering. While existing LLMs demonstrate notable proficiency in problem-solving, their capabilities are often limited when it comes to tasks demanding intricate, multi-step reasoning. This arises because conventional approaches predominantly depend on the inherent knowledge within LLMs, rendering them occasionally prone to logical fallacies or hallucinations as the reasoning steps increase.

RAG-Star is introduced as a retrieval-augmented generation (RAG) method that integrates the retrieval of external information to enhance a tree-based deliberative reasoning process. This method employs the Monte Carlo Tree Search (MCTS), a robust technique for planning which iteratively formulates intermediate sub-queries and evaluates potential answers based on the LLM's knowledge.

Key Features of RAG-Star

1. Monte Carlo Tree Search (MCTS) for Deliberative Reasoning:
   • MCTS is utilized within RAG-Star to search for possible reasoning paths by generating sub-queries and corresponding answers. This approach supports in-depth strategic decision-making akin to a 'System 2' mode of reasoning, characterized by conscious, logical planning.
2. Retrieval-Augmented Verification:
   • To amalgamate internal and external sources of knowledge, RAG-Star introduces retrieval-augmented verification. This involves employing query- and answer-aware reward modeling. These models evaluate the consistency between the generated answers and retrieved documents, providing feedback to correct the LLM's reasoning steps.
3. Handling Knowledge Conflicts:
   • The framework is designed to mitigate conflicts between the inherent knowledge of LLMs and external sources, a common issue in traditional RAG methods. By treating retrieved information as a guiding element rather than a direct input during reasoning, RAG-Star reduces knowledge interference and improves accuracy.

Experimental Evaluation

Extensive experiments conducted using Llama-3.1-8B-Instruct and GPT-4o indicate that RAG-Star significantly surpasses traditional RAG and other reasoning-enhancement methods. The proposed framework achieves performance improvements of up to 18.98% for Llama-3.1-8B and 16.19% for GPT-4o in the evaluated datasets. This underpins the efficacy of RAG-Star in leveraging both stored and retrieved knowledge, rectifying reasoning errors and ultimately arriving at more accurate solutions.

Implications and Future Directions

RAG-Star's approach to integrating retrieval into the reasoning process represents a notable step toward overcoming the limitations of current LLMs in complex tasks. By enabling models to verify and refine their reasoning through external data, RAG-Star opens pathways for improved factual reliability and logical coherence in AI systems.

For future research, exploring alternative search algorithms and fine-tuning retrieval techniques could further enhance RAG-Star's capabilities. Additionally, exploring applications across diverse reasoning scenarios, such as scientific problem-solving or legal reasoning, could reveal new potentials and specific challenges, advancing further developments in AI reasoning systems. This paper contributes robust architectural insights for AI practitioners aiming to enhance the multi-step reasoning prowess of LLMs, suggesting a promising trajectory for the development of more sophisticated AI reasoning mechanisms.