[论文] Position: Logical Soundness is not a Reliable Criterion for Neurosymbolic Fact-Checking with LLMs

## 论文概要 **研究领域**: NLP **作者**: Jason Chan, Robert Gaizauskas, Zhixue Zhao ## 中文摘要 随着大语言模型(LLM)越来越多地被整合到事实核查流程中，形式逻辑通常被提议作为一种严谨的手段来缓解这些模型输出中的偏见、错误和幻觉。例如，一些神经符号系统通过使用LLM将自然语言转换为逻辑公式，然后检查所提出的主张是否在逻辑上合理（即是否可以从经验证为真的前提中有效推导出来）来验证主张。我们认为，这种方法在结构上无法检测误导性主张，因为逻辑上合理的结论与人类通常做出和接受的推论之间存在系统性分歧。借鉴认知科学和语用学的研究，我们提出了一系列案例分类，其中逻辑上合理的结论系统性地引发了对底层前提缺乏支持的人类推论。因此，我们提倡一种互补的方法：利用LLM类人的推理倾向作为特性而非缺陷，并使用这些模型来验证神经符号系统中形式组件的输出，以防止潜在的误导性结论。 ## 原文摘要 As large language models (LLMs) are increasing integrated into fact-checking pipelines, formal logic is often proposed as a rigorous means by which to mitigate bias, errors and hallucinations in these models' outputs. For example, some neurosymbolic systems verify claims by using LLMs to translate natural language into logical formulae and then checking whether the proposed claims are logically sound, i.e. whether they can be validly derived from premises that are verified to be true. We argue that such approaches structurally fail to detect misleading claims due to systematic divergences between conclusions that are logically sound and inferences that humans typically make and accept. Drawing on studies in cognitive science and pragmatics, we present a typology of cases in which logically sound conclusions systematically elicit human inferences that are unsupported by the underlying premises. Consequently, we advocate for a complementary approach: leveraging the human-like reasoning tendencies of LLMs as a feature rather than a bug, and using these models to validate the outputs of formal components in neurosymbolic systems against potentially misleading conclusions. --- *自动采集于 2026-04-07* #论文 #arXiv #AI #小凯 #自动采集