Understand What LLM Needs: Dual Preference Alignment for Retrieval-Augmented Generation
(2406.18676)Abstract
Retrieval-augmented generation (RAG) has demonstrated effectiveness in mitigating the hallucination problem of LLMs. However, the difficulty of aligning the retriever with the diverse LLMs' knowledge preferences inevitably poses an inevitable challenge in developing a reliable RAG system. To address this issue, we propose DPA-RAG, a universal framework designed to align diverse knowledge preferences within RAG systems. Specifically, we initially introduce a preference knowledge construction pipline and incorporate five novel query augmentation strategies to alleviate preference data scarcity. Based on preference data, DPA-RAG accomplishes both external and internal preference alignment: 1) It jointly integrate pair-wise, point-wise, and contrastive preference alignment abilities into the reranker, achieving external preference alignment among RAG components. 2) It further introduces a pre-aligned stage before vanilla Supervised Fine-tuning (SFT), enabling LLMs to implicitly capture knowledge aligned with their reasoning preferences, achieving LLMs' internal alignment. Experimental results across four knowledge-intensive QA datasets demonstrate that DPA-RAG outperforms all baselines and seamlessly integrates both black-box and open-sourced LLM readers. Further qualitative analysis and discussions also provide empirical guidance for achieving reliable RAG systems. Our code is publicly available at https://github.com/dongguanting/DPA-RAG.
Overview
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The paper addresses the alignment issues within Retrieval-Augmented Generation (RAG) systems, proposing the DPA-RAG framework to synchronize retrievers and LLMs to reduce factual inconsistencies and hallucinations.
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The DPA-RAG framework includes three components: Preference Knowledge Construction, Reranker-LLM Alignment, and LLM Self-Alignment, each focusing on improving the coherence and reliability of retrieved information used by LLMs.
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Extensive experimental results on four QA datasets show that DPA-RAG outperforms traditional RAG setups and reranker-based models, emphasizing the framework's potential in enhancing factual accuracy and reliability across various applications.
Dual Preference Alignment for Retrieval-Augmented Generation: A Critical Analysis
The paper "Understand What LLM Needs: Dual Preference Alignment for Retrieval-Augmented Generation" by Guanting Dong et al., explore the critical issue of aligning retrievers and readers within Retrieval-Augmented Generation (RAG) systems to mitigate factual inconsistencies and hallucinations by LLMs. The authors introduce the DPA-RAG framework, aimed at refining the integration of LLMs and retrievers through a dual preference alignment mechanism.
Problem Statement: RAG systems, despite their utility in combining internal model information with external knowledge, often face challenges due to the divergence in model architectures, training objectives, and task formats between their components. This misalignment can lead to scenarios where retrieved documents either do not support the needed inference by LLMs or, potentially worse, actively mislead the reasoning process. Dong et al. identify this preference gap and propose a method to address it through a systematic approach that aligns both the retriever and the LLM with the LLM's intrinsic knowledge preferences.
Methodology: The authors propose a robust framework, DPA-RAG, which comprises three pivotal components:
- Preference Knowledge Construction: This involves extracting LLM-preferred knowledge from the training data, augmented by five innovative query augmentation strategies, namely Rephrasing, Complexity, Decomposition, Constraint, and SPARQL.
- Reranker-LLM Alignment: Utilizing multi-grained preference data, the reranker is fine-tuned through joint integration of pair-wise, point-wise, and contrastive preference alignment. This aims to filter and prioritize documents that align with the LLM’s preference, ensuring external alignment between RAG components.
- LLM Self-Alignment: A pre-aligned stage is introduced prior to traditional Supervised Fine-Tuning (SFT), enabling LLMs to concentrate on preference-aligned knowledge. This step ensures internal model consistency, facilitating improved utilization of retrieved documents.
Experimental Setup: The framework's efficacy is evaluated on four knowledge-intensive QA datasets: NQ, TriviaQA, HotpotQA, and WebQSP. Metrics such as Hit@1 and F1 scores are employed to measure performance, providing insights into both the retrieval accuracy and the quality of the generated responses.
Results: The study presents robust results, demonstrating that DPA-RAG consistently outperforms traditional RAG setups and reranker-based models. Key findings include:
- Significant performance improvements across all evaluated datasets and LLM models, indicating the generalizability and effectiveness of the dual-alignment approach.
- Notable reductions in the misalignment of retrieved documents, with corresponding improvements in aligned knowledge as evidenced by higher Hit@1 and F1 scores.
- The ablation study emphasizes the critical role of both the reranker alignment and LLM self-alignment stages, highlighting the synergistic benefits of integrating these components.
Implications: The implications of this research are manifold. Practically, it offers a scalable and adaptable solution to enhance the reliability of RAG systems across diverse domains, particularly in applications requiring high factual consistency. Theoretically, it underscores the importance of multi-level alignment within integrated system architectures, suggesting avenues for future exploration in multi-task optimization and preference-based learning for AI systems. The consistent performance gains across various LLM models also imply that the dual preference alignment approach could serve as a foundational paradigm for future developments in AI-driven retrieval and generation tasks.
Future Directions: Given the empirical success of DPA-RAG, future research could explore several directions:
- Extending the framework to more complex, multi-modal RAG systems involving text, image, and tabular data.
- Investigating the impact of the dual alignment methodology on real-time, interactive AI applications.
- Refining the augmentation strategies to further enhance data diversity and complexity, thereby improving model robustness.
- Exploring the integration of reinforcement learning techniques to dynamically adjust preference alignments in response to evolving data patterns and user feedback.
In conclusion, the paper presents a meticulous and impactful exploration of dual preference alignment in RAG systems, offering a novel and effective approach to bridge the gap between retrievers and LLM-based readers. The proposed DPA-RAG framework not only enhances the factual accuracy and reliability of generated content but also sets the stage for advanced research in AI alignment methodologies.
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