揭穿大模型推理神话，警示学术剽窃风险
LLMs, Reasoning and Plagiarism
- 提出可验证的透明性指南，检验大模型是否真能推理
- 指出当前所谓推理实为无引用复述他人成果
- 适合关注AI伦理与科研诚信的研究者阅读
近期报告声称大型语言模型（LLMs）能生成新科学知识并展现类人通用智能。这些说法背后存在两种叙事：一是模型作为知识合成引擎，真正进行推理以获得新认知；二是模型仅检索并重新输出他人工作而不注明来源。在科学语境下，这体现为‘推理’与‘剽窃’的对立。然而，由于训练数据和交互记录的不透明，难以判断这两种叙事何者为真，导致所谓推理声明无法满足波普尔的可证伪性原则。本文提出透明性与可复现性准则，使推理主张能通过科学方法验证。我们指出，当前对‘推理’叙事的过度推崇，实际上在实践中助长了学术文献中的剽窃行为，并探讨应对之策。
原文摘要 · Abstract (English)
Recent reports claim that Large Language Models (LLMs) derive new science and exhibit human-level general intelligence. Such claims are entangled with two different narratives about what LLMs do: one in which they are an engine of synthesis that genuinely reasons to new knowledge, and one in which they retrieve and re-emit the work of others without attribution. In the scientific setting these are best understood as a contrast between \emph{reasoning} and \emph{plagiarism}. Finding where the truth lies between these two narratives is very challenging, as central components of the model -- the training data and the interaction transcript -- remain opaque. Thus claims of LLM reasoning do not satisfy Popper's refutability principle. We propose guidelines for transparency and reproducibility that will allow reasoning claims to be studied using the scientific method. The dominance of the reasoning narrative, we suggest, is in practice encouraging plagiarism in the scientific literature; we discuss what might be done about it.
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