Improving Zero-shot Reader by Reducing Distractions from Irrelevant Documents in Open-Domain Question Answering (2023)

Abstract: Large language models (LLMs) enable zero-shot approaches in open-domain question answering (ODQA), yet with limited advancements as the reader is compared to the retriever. This study aims at the feasibility of a zero-shot reader that addresses the challenges of computational cost and the need for labeled data. We find that LLMs are distracted due to irrelevant documents in the retrieved set and the overconfidence of the generated answers when they are exploited as zero-shot readers. To tackle these problems, we mitigate the impact of such documents via Distraction-aware Answer Selection (DAS) with a negation-based instruction and score adjustment for proper answer selection. Experimental results show that our approach successfully handles distraction across diverse scenarios, enhancing the performance of zero-shot readers. Furthermore, unlike supervised readers struggling with unseen data, zero-shot readers demonstrate outstanding transferability without any training.

## introduction open domain question answering (odqa) is a task for answering questions with the evidence documents fetched from a large corpus @xcite . a retrieve-read framework has achieved remarkable performance in odqa by fine-tuning the language models with labeled datasets @xcite @xcite . the emergence of large language models (llms) has enabled the exploration of zero-shot approaches in this framework, with less emphasis on the reader component @xcite @xcite . utilizing an llm as a reader provides an advantage in the generalization ability with the rich world knowledge, unlike conventional small-sized supervised readers @xcite and grave, 2021). while the supervised readers show remarkable performance on odqa, they are hampered by two weaknesses: the high computational cost involved in training and the necessity of annotated query-document datasets. these limitations impede the transferability of readers to diverse tasks and domains. to solve this, we aim to validate the feasibility of using an llm as a reader, leveraging its inherent advantages while reducing the aforementioned limitations. however, the performance of an llm in various tasks is easily distracted by irrelevant documents @xcite , underscoring the importance of resolving these challenges in odqa. the tendency of an llm to generate incorrect answers becomes apparent when reading retrieved sets that include irrelevant documents. these documents, while related to the query, may lack the necessary information to provide an answer, leading to the occurrence of hallucination. this emphasizes the need for proper handling of such documents to fully harness the potential of an llm, thereby achieving reliable performance as a reader. this paper addresses the requisite of hallucination mitigation to validate the possibility of an llm as a zero-shot reader. in this paper, we propose distraction-aware answer selection (das), handling the challenges posed by irrelevant documents and overconfident scores as shown in figure 1 . first, we provide models with an "unanswerable" instruction, allowing them to abstain from answering. then, we adjust the answer scores by reflecting the query generation score as the relevance between the given query-document pairs. these approaches reduce the impact of irrelevant documents and improve the selection of the correct answer from the relevant document. we evaluate our proposed method on representative odqa benchmarks with two publicly open llms, flan-t5 @xcite and opt-iml-max @xcite . this results in substantial performance improvements achieved by ours compared to a naïve llm across all scenarios. note that ours effectively alleviates the hallucination induced by irrelevant documents by enhancing the robustness against the number of documents that are read. furthermore, an llm with our method exhibits excellent transferability compared to the supervised reader, offering the untapped potential of an llm as a zero-shot reader. our contributions in this paper are threefold: for the reader stage, @xcite attempted to utilize an llm as a zero-shot reader, addressing the irrelevant documents through a re-ranker. in this study, we focus on a fully zero-shot reader without an additional module. distraction from noisy input recent work addresses the negative impact of noisy inputs when exploiting an llm in diverse tasks. llms are easily distracted by the noisy input having incorrect or irrelevant information on machine reading comprehension tasks @xcite @xcite . however, the odqa task increases the complexity, where large-scale document sets appear within unrelated documents. given the impact of distracting sentences in qa @xcite @xcite , our approach aims to alleviate them. ## conclusion in this paper, we propose distraction-aware answer selection (das) to address the irrelevant documents in the retrieved set when an llm is used as a zero-shot reader. to validate its capability, we define hallucination caused by irrelevant documents and overconfident answer scores in odqa setting. ours aims to mitigate the impact of these aspects by incorporating unanswerable instruction and adjusting answer scores for better answer selection. experimental results demonstrate the effectiveness of our proposal in handling hallucination across various scenarios, thereby improving the performance of odqa benchmarks. our approach, utilizing an llm, showcases strong generalization capabilities across diverse datasets, distinguishing it from supervised readers and highlighting the potential of a zero-shot reader.