Fine-Grained Guidance for Retrievers: Leveraging LLMs' Feedback in Retrieval-Augmented Generation (2411.03957v1)

Abstract: Retrieval-Augmented Generation (RAG) has proven to be an effective method for mitigating hallucination issues inherent in LLMs. Previous approaches typically train retrievers based on semantic similarity, lacking optimization for RAG. More recent works have proposed aligning retrievers with the preference signals of LLMs. However, these preference signals are often difficult for dense retrievers, which typically have weaker language capabilities, to understand and learn effectively. Drawing inspiration from pedagogical theories like Guided Discovery Learning, we propose a novel framework, FiGRet (Fine-grained Guidance for Retrievers), which leverages the language capabilities of LLMs to construct examples from a more granular, information-centric perspective to guide the learning of retrievers. Specifically, our method utilizes LLMs to construct easy-to-understand examples from samples where the retriever performs poorly, focusing on three learning objectives highly relevant to the RAG scenario: relevance, comprehensiveness, and purity. These examples serve as scaffolding to ultimately align the retriever with the LLM's preferences. Furthermore, we employ a dual curriculum learning strategy and leverage the reciprocal feedback between LLM and retriever to further enhance the performance of the RAG system. A series of experiments demonstrate that our proposed framework enhances the performance of RAG systems equipped with different retrievers and is applicable to various LLMs.

Fine-Grained Guidance for Retrievers: Enhancing RAG Systems with LLMs

The paper "Fine-Grained Guidance for Retrievers: Leveraging LLMs' Feedback in Retrieval-Augmented Generation" explores the persistent challenge of hallucinations in LLMs and explores innovative solutions within the Retrieval-Augmented Generation (RAG) paradigm. The authors present a novel framework, FiGRet (Fine-grained Guidance for Retrievers), which aims to refine the alignment between retrievers and LLMs, enhancing overall RAG system performance.

Core Contributions

FiGRet focuses on improving retrievers by leveraging the advanced language capabilities of LLMs. The framework addresses the traditional reliance on semantic similarity for retriever training, instead optimizing for LLM preferences through a structured pedagogical approach inspired by theories such as Guided Discovery Learning. The key contributions of this work include:

1. Granular Example Construction: The framework constructs intuitive examples for retrievers, primarily targeting three objectives: relevance, comprehensiveness, and purity. These examples facilitate a deeper alignment with LLM preferences, assisting less capable retrievers in understanding complex signals.
2. Dual Curriculum Learning Strategy: By combining traditional training with reciprocal feedback between LLMs and retrievers, FiGRet adopts a strategic curriculum that progressively increases the complexity of training tasks, mirroring educational methodologies for enhanced model proficiency.
3. Objective-Based Learning: The framework is built around three minimally overlapping objectives crucial for RAG performance:
   • Relevance focuses on ensuring that retrieved documents are directly informative and useful for the LLM.
   • Comprehensiveness emphasizes retrieving documents with detailed content pertinent to the query.
   • Purity aims to minimize noise in retrieved documents, ensuring high-quality information for LLM processing.

Experimental Validation

The FiGRet framework demonstrated substantial efficacy across various tasks and LLM configurations, as evidenced by experimental results. Notably, the framework provided significant improvements in performance metrics across datasets like MMLU, open-domain QA, and fact-checking tasks. These improvements were consistent across different LLMs such as GPT-3.5-Turbo, Llama-3, and Claude-3, showcasing FiGRet's robust applicability.

Practical and Theoretical Implications

Practically, FiGRet offers a less resource-intensive methodology for enhancing RAG systems, bypassing the traditional bottleneck of aligning retrievers through extensive retraining on preference-based signals. The framework’s reliance on black-box LLMs for inference feedback indicates potential for widespread deployment across systems constrained by resource availability.

Theoretically, FiGRet contributes to understanding the dynamics of retriever-LLM interaction. By shifting focus to information-centric perspectives, the work invites further exploration into the granular alignment of model components within AI systems. This iterative process of scaffolding and feedback might influence broader AI methodologies, suggesting avenues for future research into cross-model communication and learning efficacy.

Conclusion and Future Directions

The FiGRet framework presents a paradigm shift by employing fine-grained, LLM-driven guidance for retrievers within RAG systems, enhancing output quality without necessitating large-scale retriever realignment efforts. Future research could expand on this foundation, exploring integration with alternative RAG strategies or applying similar pedagogical frameworks to different AI components for refined, goal-aligned performance across tasks. This work exemplifies a strategic step towards optimizing the harmony between multifaceted AI systems through structured, intelligent guidance.

Overall, the paper establishes a sophisticated balance between theoretical insight and practical application, positioning FiGRet as a valuable asset in the landscape of retrieval-augmented AI technologies.