arXiv cs.CL· Dave Murray-Rust, Elisa Giaccardi, Federico Bomba, Maria Luce Lupetti·· 10 小时前AI 评分20
从偏见到位格性：面向大语言模型的批判性语法
From Bias to Positionality: A critical grammar for Large Language Models
AI 导读
研究者提出用“位格性”（positionality）替代“偏见”作为理解大语言模型的语法，指模型总是从某个被定位、可争议的位置说话，而非“无处”或“无处不在”。该框架将模型位格性分为层叠结构，横跨语料、微调、系统提示词、应用与实时对话，并区分创作者位格性、预期行为与实际行为三组因素。
正文 · 原文
Abstract:Bias has become a default lens for understanding what machine learning systems do and how we should relate to them. Bias is at root a deviation from a standard; in decision making systems we can supply a standard from outside: a law, a protected attribute, a benchmark, a statistical property. However, even there, biased is often used as a shorthand, without stating what the position taken is - a silent ``relative to''. In a general use of language models, nothing supplies the standard: there is no decision to audit and no counterfactual output, so the shorthand fails. We argue that positionality -- the located, articulated, owned and contestable position from which anything is said -- is the better grammar for the question these models now force: who is speaking, from where? Against the vendors' answer of nowhere and the seductive possibility of everywhere, we argue that when a model is taken as-if speaking, it speaks from somewhere, and this means positionality can describe models, and the way they are believed beyond their warrant, even though they are not accountable speakers. A model's positionality is laminar -- laid down across corpus, tuning, system prompt, application and the live conversation. We separate three sets of factors: the positionality of a model's creators, the behaviour they intend for it, and the behaviour it enacts, and situate current work within this matrix. This lets us highlight what each stratum inherits from the one below, with ruptures and collapses where that inheritance is disavowed. We then return to where models speak from, and discuss the implications for how models are made, read and held accountable.
| Subjects: | Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Human-Computer Interaction (cs.HC) |
| Cite as: | arXiv:2611.14079 [cs.CL] |
| (or arXiv:2611.14079v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2611.14079 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Dave Murray-Rust [view email]
[v1]
Tue, 10 Nov 2026 13:41:18 UTC (212 KB)
来源：arXiv cs.CL · arxiv.org