BIDER : Bridging Knowledge Inconsistency for Efficient Retrieval-Augmented LLM s via Key Supporting Evidence (2024)

Abstract: Retrieval-augmented large language models (LLMs) have demonstrated efficacy in knowledge-intensive tasks such as open-domain QA, addressing inherent challenges in knowledge update and factual inadequacy.However, inconsistencies between retrieval knowledge and the necessary knowledge for LLMs, leading to a decline in LLM’s answer quality. This paper introduces BIDER, an approach that refines retrieval documents into Key Supporting Evidence (KSE) through knowledge synthesis, supervised fine-tuning (SFT), and preference alignment. We train BIDER by learning from crafting KSE, while maximizing its output to align with LLM’s information acquisition preferences through reinforcement learning. Evaluations across five datasets show BIDER boosts LLMs’ answer quality by 7% while reducing input content length in retrieval documents by 80%, outperforming existing methods. The proposed KSE simulation effectively equips LLMs with essential information for accurate question answering.

## introduction large language models (llms) are currently developing rapidly and showing tremendous capabilities @xcite . nevertheless, they face challenges in knowledge updates and furnishing factual responses @xcite , especially in knowledge-intensive tasks like open-domain qa @xcite . to address these issues, retrieval-augmented generation (rag) has emerged as a promising approach @xcite @xcite . retrievalaugmented methodologies serve to mitigate the drawbacks of llms by incorporating external knowledge, thereby enhancing the quality and reliability of generated answers @xcite @xcite . the standard rag procedure involves retrieving pertinent documents related to a given question and subsequently inputting these documents as auxiliary information directly into the prompt. this strategic utilization enables the model to capitalize on its advanced text comprehension skills, facilitating the generation of precise and contextually appropriate answers. however, retrieval-augmented llms are not always beneficial. due to imperfections in the retrieval system and the inaccessibility of llm's self-knowledge @xcite , the retrieved documents provided to llm are frequently lengthy and noisy, which can detrimentally affect generation quality @xcite . recognizing this decline, recent researches have made strides in optimizing retrieved documents. these efforts aim to mitigate noise in retrieved documents by employing sorting mechanisms to retain the most pertinent sentences @xcite , summarizing the retrieved text @xcite , and eliminating content that contributes minimally to the model's understanding @xcite or hinders effective generation @xcite . while prior methods have shown progress in enhancing the quality of retrieved documents, they often rely excessively on feedback from the generator, overlooking the essential knowledge required for addressing the questions themselves. this overreliance on llm's feedback is not only insufficient but also susceptible to the instability of llm feedback, potentially resulting in the loss of crucial information and the retention of noisy elements. we argue that this limitation might stem from the neglect of knowledge inconsistency between the retrieved results and the knowledge truly required by the model for answering the question. we term this essential knowledge as key supporting evidence (kse). as shown in figure 1 , due to the imperfections of the retrieval system and the inaccessibility of llm's self-knowledge @xcite , retrieved results often contain numerous noise elements beyond key supporting evidence. to address the aforementioned knowledge inconsistency issue, we propose bider(bridging knowledge inconsistency for efficient retrievalaugmented llms), a method designed to refine retrieval documents into kse. the overall training process of bider consists of three stages, integrating the strengths of both supervised and reinforcement learning, as shown in figure 2 . in the knowledge synthesizing stage, we employ a meticulous three-step process to synthesize authentic kse. in the supervised fine-tuning stage, we construct a seq2seq model to learn the mapping from retrieved documents to kse. finally, in the preference alignment stage, we leverage reinforcement learning techniques to align the developed model with the preferences of the downstream llm. this alignment ensures that the refined retrieval documents contain coherent and easily digestible key information, which is crucial for the llm to generate accurate and informative responses. we evaluate the effectiveness of our method on five datasets from three types of knowledgeintensive tasks, i.e., nq, tqa, and hotpotqa for open-domain qa, wow for dialogue generation, and fever for fact verification. results show that our method achieves better generation performance while reducing the input information length by 80%, effectively condensing retrieved documents, and outperforming existing methods. we also validate the advantages of our proposed kse data construction process and investigate the impact of the preference alignment stage on the final results. furthermore, we validate the robustness of our approach under various text retrieval quality conditions. the main contributions of this work are: (1) we propose a three-step knowledge synthesis method to generate oracle kse. (2) we introduce a method to refine retrieval documents into kse, thereby bridging knowledge inconsistencies between retrieval documents and the knowledge required by llms for answering. (3) we train the refiner model using supervised distillation and preference alignment techniques, efficiently enhancing rag performance during inference by reducing input length and improving answer quality. ## bider: a knowledge refiner for rag our objective is to furnish the necessary knowledge for a generator, specifically the kse as defined earlier, to answer a question. since authentic kse is unattainable, we employ a synthesize-and-learn paradigm. we design a method for synthesizing authentic kse, training the refiner to learn the map from retrieval documents to constructed kse, and adapting the model's information acquisition preferences based on the generator's feedback. the overall framework of bider is illustrated in figure 2 . in this section, we first formulate the research problem ( §3.1). then, we introduce the details of three training stages of bider, including knowledge synthesis ( §3.2), supervised distillation ( §3.3), and preference alignment ( §3.4). ## conclusion we present bider, a method to refine retrieved documents into kse, addressing inconsistencies between retrieved results and the knowledge needed by the generator. we designed a three-step process to synthesize authentic key supporting evidence to enhance the effectiveness of supervised learning, while utilizing llm's feedback for further alignment. through a well-structured training process, bider effectively provides the generator with the necessary information to answer questions based on the original retrieval text, achieving a significant improvement in answer quality while reducing input length by 80%.