CORAG: A Cost-Constrained Retrieval Optimization System for Retrieval-Augmented Generation (2411.00744v1)

Abstract: LLMs have demonstrated remarkable generation capabilities but often struggle to access up-to-date information, which can lead to hallucinations. Retrieval-Augmented Generation (RAG) addresses this issue by incorporating knowledge from external databases, enabling more accurate and relevant responses. Due to the context window constraints of LLMs, it is impractical to input the entire external database context directly into the model. Instead, only the most relevant information, referred to as chunks, is selectively retrieved. However, current RAG research faces three key challenges. First, existing solutions often select each chunk independently, overlooking potential correlations among them. Second, in practice the utility of chunks is non-monotonic, meaning that adding more chunks can decrease overall utility. Traditional methods emphasize maximizing the number of included chunks, which can inadvertently compromise performance. Third, each type of user query possesses unique characteristics that require tailored handling, an aspect that current approaches do not fully consider. To overcome these challenges, we propose a cost constrained retrieval optimization system CORAG for retrieval-augmented generation. We employ a Monte Carlo Tree Search (MCTS) based policy framework to find optimal chunk combinations sequentially, allowing for a comprehensive consideration of correlations among chunks. Additionally, rather than viewing budget exhaustion as a termination condition, we integrate budget constraints into the optimization of chunk combinations, effectively addressing the non-monotonicity of chunk utility.

Overview of CORAG: A Cost-Constrained Retrieval Optimization System for Retrieval-Augmented Generation

The paper "CORAG: A Cost-Constrained Retrieval Optimization System for Retrieval-Augmented Generation" presents a methodological advancement in the field of Retrieval-Augmented Generation (RAG) by addressing three notable challenges: chunk correlation, non-monotonic utility of chunk inclusion, and the diversity of user queries. The authors propose the CORAG system which leverages a Monte Carlo Tree Search (MCTS)-based framework to improve retrieval efficiency in RAG systems, with a focus on optimizing chunk combinations under cost constraints.

Key Challenges in RAG Systems

1. Chunk Correlation and Redundancy
   • Previous RAG approaches often treated each retrieved chunk independently or uniformly aggregated by clusters, leading to a disregard for inter-chunk correlations. This can introduce substantial redundancy, as multiple chunks may convey overlapping information. By overlooking the interrelations among chunks, RAG systems might miss assembling chunks into the most informative and necessary combinations to address specific user queries effectively.
2. Non-Monotonicity of Chunk Utility
   • Traditional methods that maximize the number of chunks selected under the assumption that more information equates to better utility have been shown to be suboptimal. Excessive inclusion of chunks can introduce noise and dilute the informational purity due to utility non-monotonicity, where adding chunks might degrade the model's generation quality. Therefore, an approach that considers the diminishing returns of chunk addition is critical for enhancing RAG performance.
3. Diversity in Query Characterization
   • Different user queries inherently have unique requirements when retrieving pertinent information. The lack of tailored retrieval strategies for each query type in existing systems leads to inefficiencies. For practical applications, RAG systems need to incorporate query-specific behaviors to enhance adaptability.

Contributions of CORAG

• MCTS-Based Framework for Optimal Chunk Selection
  • CORAG introduces a novel MCTS approach to simultaneously handle the challenges of chunk correlation and non-monotonicity. The system employs MCTS to determine optimal chunk combinations and the order of chunks, allowing the inference process to account for inter-chunk relationships extensively and dynamically.
• Integration of Cost Constraints in Retrieval Optimization
  • By embedding the cost considerations directly into the optimization routines, the system dynamically balances the trade-off between chunk inclusion benefits and computational costs, eschewing traditional budget exhaustion methods to terminate the retrieval process.
• Configuration Agent for Query Adaptation
  • To address the diversity of query characteristics, CORAG involves a configuration agent that determines optimal MCTS configurations based on the query type. This agent utilizes contrastive learning to fine-tune and adapt retrieval behavior to the query domain, which enhances both the retrieval effectiveness and the efficiency of generated responses.

Experimental Results

CORAG demonstrates significant improvements in retrieval effectiveness and efficiency over existing baseline models in the experiments conducted. Notably, the framework achieved up to a 30% enhancement in performance metrics compared to traditional top-k selection and clustering methods. These results highlight CORAG's capability in managing long-context tasks while maintaining scalability and cost-effectiveness in computational resources.

Implications and Future Directions

The proposed CORAG system sets a precedent for addressing retrieval efficiency with a comprehensive approach that considers the intricacies of chunk selection beyond conventional methods. Its successful application to RAGs suggests potential extensions to other domains requiring retrieval optimizations where cost constraints and dynamic query handling are pertinent. Future developments could involve further enhancing MCTS strategies or embedding similar methodologies in other aspects of RAG components.

Overall, the CORAG system demonstrates a robust and well-documented evolution in RAG processes, offering a balanced model for enhancing retrieval accuracy against computational limits while catering to the diversity of user queries and contextual requirements.