探讨大模型能否真正自我反思，发现部分自述只是程序输出。
Does It Make Sense to Speak of Introspection in Large Language Models?
- 分析两个大模型自述案例，区分真实反思与模仿行为。
- 模型能准确推断自身温度参数，属最小化自我认知。
- 适合关注AI意识与语言能力边界的研究者阅读。
大型语言模型（LLMs）展现出令人信服的语言行为，有时会提供关于自身本质、内在机制或行为的自我报告。在人类中，这类报告通常归因于内省能力，并与意识相关。这引发了一个问题：如何解释日益流利且具备认知能力的LLMs所生成的自我报告？在多大程度上（如果有）可以有意义地将内省概念应用于LLMs？本文提出并批判性分析了两个看似内省的自我报告例子。第一个例子中，一个LLM试图描述其‘创造性’写作过程，我们认为这不是有效的内省实例。第二个例子中，一个LLM正确推断出自身温度参数的值，我们认为这可被合理视为一种最小形式的内省，尽管它可能不伴随意识体验。
原文摘要 · Abstract (English)
Large language models (LLMs) exhibit compelling linguistic behaviour, and sometimes offer self-reports, that is to say statements about their own nature, inner workings, or behaviour. In humans, such reports are often attributed to a faculty of introspection and are typically linked to consciousness. This raises the question of how to interpret self-reports produced by LLMs, given their increasing linguistic fluency and cognitive capabilities. To what extent (if any) can the concept of introspection be meaningfully applied to LLMs? Here, we present and critique two examples of apparent introspective self-report from LLMs. In the first example, an LLM attempts to describe the process behind its own "creative" writing, and we argue this is not a valid example of introspection. In the second example, an LLM correctly infers the value of its own temperature parameter, and we argue that this can be legitimately considered a minimal example of introspection, albeit one that is (presumably) not accompanied by conscious experience.
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