REAR : A Relevance-Aware Retrieval-Augmented Framework for Open-Domain Question Answering (2024)

Abstract: Considering the limited internal parametric knowledge, retrieval-augmented generation (RAG) has been widely used to extend the knowledge scope of large language models (LLMs). Despite the extensive efforts on RAG research, in existing methods, LLMs cannot precisely assess the relevance of retrieved documents, thus likely leading to misleading or even incorrect utilization of external knowledge (i.e., retrieved documents). To address this issue, in this paper, we propose REAR, a RElevance-Aware Retrieval-augmented approach for open-domain question answering (QA). As the key motivation, we aim to enhance the self-awareness regarding the reliability of external knowledge for LLMs, so as to adaptively utilize external knowledge in RAG systems. Specially, we develop a novel architecture for LLM based RAG system, by incorporating a specially designed assessnent module that precisely assesses the relevance of retrieved documents. Furthermore, we propose an improved training method based on bi-granularity relevance fusion and noise-resistant training. By combining the improvements in both architecture and training, our proposed REAR can better utilize external knowledge by effectively perceiving the relevance of retrieved documents. Experiments on four open-domain QA tasks show that REAR significantly outperforms previous a number of competitive RAG approaches. Our codes can be accessed at https://github.com/RUCAIBox/REAR.

## introduction despite the progressive capacities, large language models (llms) @xcite still struggle with knowledge-intensive tasks like open-domain question answering (qa), lacking in real-time and domain knowledge @xcite . to mitigate this figure 1 : llms may be misled by irrelevant documents, and struggle to determine the relevance of a document @xcite . while rag offers clear benefits, it also introduces several technical challenges for effectively improving llms. firstly, the retrieved results likely contain irrelevant content or documents, which may mislead llms and even cause them to respond incorrectly @xcite . moreover, it has become common to incorporate multiple reference documents to boost the overall reliability of retrieved documents. however, this approach potentially amplifies the impact of the noise present in the retrieved documents @xcite . thus, llms face difficulties in filtering irrelevant documents and integrating their internal knowledge @xcite , which needs to avoid potential interference with noisy content. recently, several studies @xcite @xcite have attempted to enhance the robustness of rag systems. for instance, self-rag @xcite allows the model to introspect its outputs by generating special tokens to discriminate if the documents are relevant, and robustlm @xcite prompts llms to first discriminate if the documents are rel-evant and then generate answers. however, these approaches perform the assessment of document relevance solely based on binary labels, which are highly sparse and not precise to capture the finegrained relevance. in addition, they seldom consider the varied relevance degree of reference documents, making the utilization of external knowledge somehow blind. to this end, in this paper, we propose rear, a relevance-aware retrieval-augmented generation approach for open-domain question answering (qa). our key idea is to develop robust selfawareness regarding the reliability of external knowledge (i.e., retrieved documents) within rag systems, so that the llm can learn to adaptively utilize the internal and external knowledge for solving complex qa tasks. to achieve this goal, we make two major contributions in both model architecture and training. first, we propose relevance-aware rag architecture by incorporating explicit assessment modules in llms' generation architecture to perform an additional relevance assessment task. in our architecture, the assessment module effectively captures relevance signals, and feeds them back to avoid distractions from irrelevant external knowledge during generation. secondly, to support the relevance-aware rag architecture, we further propose two training strategies. bi-granularity relevance fusion strategy further integrates both coarse and fine-grained relevance supervision to overcome the limitations of binary discriminative methods, while noise-resistant training strategy enhances the discrimination ability of the llm by incorporating negatives in the training procedure. to the best of our knowledge, we are the first to introduce the idea of incorporating explicit assessment modules in the generation architecture of llms to aid in irrelevance-resistant generation. extensive experiments on public open-domain qa benchmarks attest to the effectiveness of our rear framework. notably, we also demonstrate the strong generalization capability of rear by conducting out-of-domain evaluation on multiple opendomain qa benchmarks. retrieval-augmented llms. several research aims at aligning the retriever outputs with the preferences of the llms @xcite . and works like atlas @xcite , ra-dit @xcite jointly train the language model and the retriever for advanced performance on rag. some other work improves the quality of retrieved documents by expanding the knowledge sources @xcite or query rewriting @xcite . however, we focus on a scenario where the irrelevant documents from retrieval could mislead llms. several recent studies @xcite @xcite attempt to adopt a paradigm in which an initial judgment on relevance is made by generating a statement or special token before proceeding to content generation. however, these methods still lack accuracy in relevance discrimination and llms are still vulnerable to irrelevant document interference. therefore, we propose a framework that can accurately assess the relevance degree, and is more robust to irrelevant content. ## task formulation in this work, we focus on the task of open-domain question answering (qa) @xcite , aiming at answering questions using a large collection of documents. typically, opendomain qa tasks are often tackled with a retrieverreader approach (chen and yih, 2020), where the retriever finds relevant evidence and the reader generates the answer based on the retrieved evidence. formally, given a query q, the retriever outputs top-k documents @xmath0. different from prior studies that combine the entire set of retrieved documents as a unified reference for answer generation @xcite @xcite , our approach emphasizes individual document uti- golden document likely to be false negative irrelevant docs we sample likely to be less informative train @xmath1, @xmath2, @xmath3, @xmath4.  shared parameters assessment module generation module query knowledge degree generate answer ground truth step 1: relevance assessment. evaluate the reliability degree of the external knowledge. step 2: relevance-guided generation. generate answer based on the reliability degree. train degree assessment module generation module shared parameters query knowledge answer step 3: knowledge reliability verification. verify the consistency between parametric and external knowledge. greedy @xmath5.9 relevant +0.3 irrelevant -0.8 label machine label machine bi-granularity relevance fusion noise-resistant training sampling training objectives lization, which can be also extended to a multidocument setting. given the input query q and reference documents @xmath6, the reader (i.e., the llm) generates an answer a i based on each reference document d i , forming an answer set a: subsequently, we can choose the final answer from a based on some specific ways, ensuring it aligns best with the query q. based on this task formulation, we consider enhancing two key aspects: precise evaluation of relevance between queries and documents (identifying relevant references), and leveraging relevance signal for noise-resistant generation (reducing the influence of irrelevant content). therefore, we introduce a relevance-aware approach designed specifically for these challenges. ## methodology in this section, we present the proposed relevance-aware retrieval-augmented generation framework (rear), which is capable of precisely assessing the relevance degree during the generation process by incorporating explicit assessment modules within the llm. furthermore, we propose optimized training methods that are compatible with the rear framework to support efficient operation, including bi-granularity relevance fusion and noise-resistant training. ## conclusion in this paper, we aimed to enhance the selfawareness of source relevance in rag systems, and proposed rear, a relevance-aware retrievalaugmented approach for open-domain question answering (qa). for model architecture, we explicitly integrate an assessment module to precisely capture the relevance signals, and employ it to guide the utilization of external knowledge. for model training, we designed an improved training method with bi-granularity relevance fusion and noise-resistant training, which enhance the capacities of fine-grained relevance assessment and adaptive use of retrieved documents. our data construction strategy collects high-quality data without access to gpt apis. extensive experiments on four datasets demonstrate the effectiveness and generalization of rear's knowledge utilization. as future work, we will extend the proposed approach rear to deal with more fine-grained source utilization (e.g., passage or sentence level augmentation), and also consider applying rear to other knowledge-intensive tasks.