超越真伪判断：基于检索增强的层次化细粒度主张分析

摘要

个人或实体提出的主张往往具有细微差别，难以简单地归类为完全“真实”或“虚假”——这在科学和政治主张中尤为常见。然而，一个主张（例如，“疫苗A优于疫苗B”）可以被分解为其核心方面和子方面（例如，有效性、安全性、分发），这些方面单独验证起来更为容易。这种方法能够提供更为全面、结构化的回应，不仅为特定问题提供了全方位的视角，还允许读者优先关注主张中的特定角度（例如，对儿童的安全性）。因此，我们提出了ClaimSpect，这是一个基于检索增强生成的框架，旨在自动构建处理主张时通常考虑的方面层次结构，并通过特定语料库的视角对其进行丰富。该结构层次化地划分输入语料库以检索相关片段，这些片段有助于发现新的子方面。此外，这些片段还能揭示对主张某一方面的不同观点（例如，支持、中立或反对）及其各自的普遍性（例如，“有多少生物医学论文认为疫苗A比B更易于运输？”）。我们将ClaimSpect应用于我们构建的数据集中涵盖的多种现实世界科学和政治主张，展示了其在解构复杂主张和表示语料库内观点方面的鲁棒性和准确性。通过现实案例研究和人工评估，我们验证了其在多个基线方法之上的有效性。

English

Claims made by individuals or entities are oftentimes nuanced and cannot be clearly labeled as entirely "true" or "false" -- as is frequently the case with scientific and political claims. However, a claim (e.g., "vaccine A is better than vaccine B") can be dissected into its integral aspects and sub-aspects (e.g., efficacy, safety, distribution), which are individually easier to validate. This enables a more comprehensive, structured response that provides a well-rounded perspective on a given problem while also allowing the reader to prioritize specific angles of interest within the claim (e.g., safety towards children). Thus, we propose ClaimSpect, a retrieval-augmented generation-based framework for automatically constructing a hierarchy of aspects typically considered when addressing a claim and enriching them with corpus-specific perspectives. This structure hierarchically partitions an input corpus to retrieve relevant segments, which assist in discovering new sub-aspects. Moreover, these segments enable the discovery of varying perspectives towards an aspect of the claim (e.g., support, neutral, or oppose) and their respective prevalence (e.g., "how many biomedical papers believe vaccine A is more transportable than B?"). We apply ClaimSpect to a wide variety of real-world scientific and political claims featured in our constructed dataset, showcasing its robustness and accuracy in deconstructing a nuanced claim and representing perspectives within a corpus. Through real-world case studies and human evaluation, we validate its effectiveness over multiple baselines.

超越真伪判断：基于检索增强的层次化细粒度主张分析

Beyond True or False: Retrieval-Augmented Hierarchical Analysis of Nuanced Claims

摘要

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