AI 与机器学习2025arxiv.org
将自然语言翻译为一阶逻辑以检测逻辑谬误
Translating natural language to first-order logic for logical fallacy detection
进阶 · 约 60 分钟读完前置知识：一阶逻辑基础、大语言模型基础、逻辑谬误概念
导读
这篇论文研究如何将自然语言自动翻译成一阶逻辑（FOL），并利用这种形式化表示来检测文本中的逻辑谬误。作者提出了 NL2FOL 方法，借助大语言模型完成翻译，再通过逻辑推理判断论证是否成立。
HN 关注点：用形式逻辑自动检测谬误的可行性，讨论焦点是自然语言语义能否被简单的一阶逻辑公式准确刻画。
HN 评论精选
这几条评论里：质疑 2 补充 2
补充认为谬误检测有助于对抗宣传，但还需补充对价值观歪曲的检测
我不熟悉一阶逻辑这种形式化方法，很想看看它的实际效果。我觉得这是解决宣传/舆论操纵问题的重要一环。另一部分是价值观的模糊化：如果 Joe 说钉子不好，含义可能完全不同——取决于他是盖房子的木匠偏好螺丝，还是反发展主义者认为人人都该住泥屋。宣传常常构建一个逻辑上自洽、却完全歪曲某人价值观的叙事，而谬误往往出现在叙事的边界处。我们既需要能检测逻辑谬误的系统，也需要能检测价值体系不一致的系统。
FloorEgg查看英文原文
Not familiar with FOL as a formalism, and would love to see this in action. I feel like it's a big part of the solution to propaganda. The other part seems to be values obfuscation, and I wonder if this would help with that too. If Joe says that nails are bad, it can mean very different things if Joe builds houses for a living and prefers screws, or if Joe is anti development and thinks everyone should live in mud huts. Propaganda will often cast a whole narrative that can be logically consistent, but entirely misrepresents a person or people's values (their motivations and the patterns that explain their actions), and there will be logical fallacies at the boundaries of the narrative. We need systems that can detect logical fallacies, as well as value system inconsistencies.
质疑质疑简单 FOL 翻译无法刻画自然语言语义的复杂性，引用 Montague 语义和博弈语义
我确信自然语言的语义远比论文中这些看似非常临时拼凑、翻译成相对简单 FOL 公式的方法所能涵盖的更复杂。从严格形式化角度理解自然语言语义的常见做法是 Montague 语义学——大致来说，通常需要同时处理多种'模态'，每一种在普通 FOL 视角下都引入各自抽象的'可能世界'（例如某个主体的信念集合）以及这些世界之间的关联方式。更复杂的情形往往会退化为某种通用的'博弈语义'，即把任何自然语言的使用看作一场有自己（可能非常临时）规则的'博弈'。维特根斯坦对这两类进路都给出过很好的描述。
zozbot234查看英文原文
I'm pretty sure that the semantics of natural language are a lot more complex than can be accounted for by these seemingly very ad-hoc translations into comparatively straightforward FOL formulas, as are given in this paper. A common approach for the understanding of NL semantics from a strictly formal POV is Montague semantics https://en.wikipedia.org/wiki/Montague_grammar https://plato.stanford.edu/entries/montague-semantics/ - even a cursory look at these references is enough to clarify the level of complexity that's involved. Very loosely speaking one generally has to work with multiple "modalities" at the same time each of which, when understood from the POV of ordinary FOL, introduces its own separate notion of abstract "possible worlds" (representing, e.g. an agent's set of beliefs) and ways in which these "worlds" can relate to one another. More complex cases will usually degenerate in some sort of very generic "game semantics" https://en.wikipedia.org/wiki/Game_semantics https://plato.stanford.edu/entries/logic-games/ where any given use of natural language is merely seen as a "game" (in the abstract strategic, game-theoretical sense) with its own set of possibly very ad-hoc 'rules'. The philosopher Ludwig Wittgenstein https://en.wikipedia.org/wiki/Ludwig_Wittgenstein https://plato.stanford.edu/entries/wittgenstein/ gave quite a good description of both of these approaches (from a very naïve approach based on a supposedly straightforward translation to some kind of abstract logic, to a far richer one based on notions of strategies and games) to a "formal" understanding of natural language, throughout his extensive philosophical inquiry. Which is to say, I'm not sure how this paper's results are generally expected to be all that useful in practice.
补充引用莱布尼茨'让我们计算'的名言，呼应用形式化计算解决争论的传统
是莱布尼茨最早设想用计算终结哲学争论：'如果产生争议，两位哲学家之间就无需再辩论，只需像两位计算者那样拿起笔坐在算盘前，彼此说一句：让我们来计算吧。'
ColinWright查看英文原文
It was Gottfried Leibniz who envisaged the end of philosophic disputes, replacing argument with calculation. "if controversies were to arise, there would be no more need of disputation between two philosophers than between two calculators. For it would suffice for them to take their pencils in their hands and to sit down at the abacus, and say to each other (and if they so wish also to a friend called to help): Let us calculate."
质疑指出开源代码缺少预训练模型，复现有困难
论文链接了代码仓库 https://github.com/lovishchopra/NL2FOL，但我在里面没找到预训练模型，所以不确定该给 `your_nli_model_name` 参数传什么。
rahimnathwani查看英文原文
The paper links the code repo: https://github.com/lovishchopra/NL2FOL But I don't see a pretrained model in there, so I'm not sure what to pass as `your_nli_model_name`: python3 src/nl_to_fol.py --model_name <your_model_name> --nli_model_name <your_nli_model_name> --run_name <run_name> --dataset --length
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