空能指问题：为大型语言模型中“对齐”的操作化寻求更清晰的范式
The Empty Signifier Problem: Towards Clearer Paradigms for Operationalising "Alignment" in Large Language Models
AI总结：
本文从后结构主义视角提出框架，明确对齐操作化的维度与定义归属，以促进LLM对齐研究的透明与批判性评估。
摘要：
本文通过后结构主义社会政治理论的视角，探讨大型语言模型（LLMs）中“对齐”的概念，特别审视其与空能指的相似之处。为了在经验数据集中如何操作化抽象的对齐概念方面建立共享词汇，我们提出了一个框架，该框架界定了：1）模型行为的哪些维度被视为重要，然后2）这些维度的含义和定义如何被赋予，以及由谁赋予。我们定位了现有的经验文献，并就决定遵循哪种范式提供指导。通过这一框架，我们旨在培养透明和批判性评估的文化，帮助社区应对将LLMs与人类群体对齐的复杂性。
英文摘要：
In this paper, we address the concept of "alignment" in large language models (LLMs) through the lens of post-structuralist socio-political theory, specifically examining its parallels to empty signifiers. To establish a shared vocabulary around how abstract concepts of alignment are operationalised in empirical datasets, we propose a framework that demarcates: 1) which dimensions of model behaviour are considered important, then 2) how meanings and definitions are ascribed to these dimensions, and by whom. We situate existing empirical literature and provide guidance on deciding which paradigm to follow. Through this framework, we aim to foster a culture of transparency and critical evaluation, aiding the community in navigating the complexities of aligning LLMs with human populations.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
