超越语言：一种用于解释大语言模型的数学框架
Beyond Words: A Mathematical Framework for Interpreting Large Language Models
AI总结：
该研究提出用于解释大语言模型的数学框架Hex，厘清幻觉、对齐、思维链等关键概念，区分思维链推理与提示并明确二者等价条件，为生成式AI研究与落地提供形式化工具支撑。
摘要：
大语言模型（Large Language Models, LLMs）是功能强大的AI工具，能够生成和理解自然语言文本及其他复杂信息。但该领域缺乏一套可系统描述、比较和改进LLMs的数学框架。我们提出Hex——一个用于厘清LLM研究中关键术语与概念（如幻觉、对齐、自验证和思维链推理）的框架。Hex框架提供了一种精确且一致的方式来表征LLMs、识别其优缺点，并整合新的研究发现。借助Hex，我们区分了思维链推理与思维链提示，并确立了二者等价的条件。这一区分厘清了思维链提示背后的基本假设，以及它对自验证、提示编程等使用该技术的方法的影响。\n我们的目标是为LLMs提供一个形式化框架，帮助研究人员和从业者探索生成式AI的新可能性。我们并未声称拥有最终解决方案，而是将其作为开辟新研究路径的工具。我们认为，我们的形式化定义与研究结果对于推进关于如何构建安全、可靠、公平且鲁棒的生成式AI系统的讨论至关重要，尤其是在医疗保健和软件工程等领域。
英文摘要：
Large language models (LLMs) are powerful AI tools that can generate and comprehend natural language text and other complex information. However, the field lacks a mathematical framework to systematically describe, compare and improve LLMs. We propose Hex a framework that clarifies key terms and concepts in LLM research, such as hallucinations, alignment, self-verification and chain-of-thought reasoning. The Hex framework offers a precise and consistent way to characterize LLMs, identify their strengths and weaknesses, and integrate new findings. Using Hex, we differentiate chain-of-thought reasoning from chain-of-thought prompting and establish the conditions under which they are equivalent. This distinction clarifies the basic assumptions behind chain-of-thought prompting and its implications for methods that use it, such as self-verification and prompt programming. Our goal is to provide a formal framework for LLMs that can help both researchers and practitioners explore new possibilities for generative AI. We do not claim to have a definitive solution, but rather a tool for opening up new research avenues. We argue that our formal definitions and results are crucial for advancing the discussion on how to build generative AI systems that are safe, reliable, fair and robust, especially in domains like healthcare and software engineering.
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