让检索模型理解选项，更准找到答题依据
Options-Aware Dense Retrieval for Multiple-Choice query Answering
- 用选项嵌入引导检索，模拟正确答案的查询特征
- 在QuALITY数据集上准确率超越现有方法
- 适合需要长文本推理的多选题问答任务
长文本多选题问答任务需对大量文本进行稳健推理。由于大多数预训练Transformer模型一次只能处理数百词，成功完成此类任务通常依赖于识别支持正确答案的证据片段（如句子）。该领域以往研究主要采用预训练密集检索模型，因缺乏监督信号难以微调检索过程。本文提出一种新方法——选项感知密集检索（OADR），通过利用查询-选项嵌入来微调检索，旨在模仿与正确答案配对的最优查询嵌入，从而更精准地识别支持性证据。在QuALITY基准数据集上的实验表明，所提模型在性能和准确率方面均优于现有基线。
原文摘要 · Abstract (English)
Long-context multiple-choice question answering tasks require robust reasoning over extensive text sources. Since most of the pre-trained transformer models are restricted to processing only a few hundred words at a time, successful completion of such tasks often relies on the identification of evidence spans, such as sentences, that provide supporting evidence for selecting the correct answer. Prior research in this domain has predominantly utilized pre-trained dense retrieval models, given the absence of supervision to fine-tune the retrieval process. This paper proposes a novel method called Options Aware Dense Retrieval (OADR) to address these challenges. ORDA uses an innovative approach to fine-tuning retrieval by leveraging query-options embeddings, which aim to mimic the embeddings of the oracle query (i.e., the query paired with the correct answer) for enhanced identification of supporting evidence. Through experiments conducted on the QuALITY benchmark dataset, we demonstrate that our proposed model surpasses existing baselines in terms of performance and accuracy.
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