This paper aims to show that a simple framework, utilizing basic formalisms
from set theory and category theory, can clarify and inform our theories of the
relation between mind and matter.

本文旨在通过使用集合论和范畴论的基本形式理论，展示一个简单的框架，以澄清和指导我们关于心智与物质关系的理论。

重构心身形象：将形式系统应用于心与物质的关系

Reframing the Mind-Body Picture: Applying Formal Systems to the  Relationship of Mind and Matter

Hallucinations in large language models (LLMs) refer to the phenomenon of
LLMs producing responses that are coherent yet factually inaccurate. This issue
undermines the effectiveness of LLMs in practical applications, necessitating
research into detecting and mitigating hallucinations of LLMs. Previous studies
have mainly concentrated on post-processing techniques for hallucination
detection, which tend to be computationally intensive and limited in
effectiveness due to their separation from the LLM's inference process. To
overcome these limitations, we introduce MIND, an unsupervised training
framework that leverages the internal states of LLMs for real-time
hallucination detection without requiring manual annotations. Additionally, we
present HELM, a new benchmark for evaluating hallucination detection across
multiple LLMs, featuring diverse LLM outputs and the internal states of LLMs
during their inference process. Our experiments demonstrate that MIND
outperforms existing state-of-the-art methods in hallucination detection.

LLMs 出现的幻觉指的是 LLMs 产生的回应在逻辑上是连贯的，但事实上是不准确的。本文引入了一种名为 MIND 的无监督训练框架，利用 LLMs 的内部状态实时检测幻觉，无需手动注释，并提出了用于评估多个 LLMs 幻觉检测的新基准 HELM。我们的实验证明，MIND 在幻觉检测方面优于现有的最先进方法。