发表机构
Scale AI; University of California, Santa Barbara（Scale人工智能公司; 加州大学圣巴巴拉分校）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究指出优化并非全部，以GPT - 2为例，通过追踪其预训练等环节的优化文化，发现优化程序虽能测文本可能性，但无法区分不可能性是错误还是创新，却在五年内获设定语言协议权威。
AI 中文摘要
2019年，OpenAI发布了两百万个GPT - 2输出文本，这些文本不符合语法且部分损坏，旨在帮助检测机器生成的文本。产生更流畅后续版本的对齐通常被视为一项工程成就；我们却将其视为优化文化的最新表现：这种信念早于技术，即沿预定义轴的可测量改进穷尽了价值问题。通过追踪从预训练、解码、偏好调整、基准测试、界面等方面的这种信念，并追溯其在审计社会中的谱系，我们得出极限：优化程序可以衡量一段生成文本的可能性有多小；但它无法判断这种不可能性是错误还是创新。一个无法做出这种区分的程序在短短五年内却获得了设定合法语言协议的权威。几个世纪以来由学术机构、教室、语法和考官所掌握的这种权威，如今已交给了损失函数、奖励模型、基准和系统提示：一种执行判断功能却没有判断能力的工具。
英文摘要
In 2019, OpenAI released two million GPT-2 outputs-ungrammatical, half broken-to aid the detection of machine-generated text. The alignment that produced their more fluent successors is usually regarded as an engineering achievement; we read it instead as the newest expression of optimization culture: the conviction, older than the technology, that measurable improvement along predefined axes exhausts the question of value. Tracing that conviction through the stack-pretraining, decoding, preference tuning, benchmarking, interface-and back through its genealogy in the audit society, we arrive at the limit: an optimization procedure can measure how improbable a piece of generated text is; it cannot tell whether that unlikelihood is error or invention. A procedure that cannot make that distinction has nonetheless, within half a decade, assumed the authority to set the protocols of legitimate language. Held for centuries by academies and schoolrooms, grammars and examiners, this authority has been given over to loss functions, reward models, benchmarks, and system prompts: an apparatus that executes the office of judgment with no capacity for judging.
CommentsThis essay will be forthcoming in MFS Modern Fiction Studies, published by JHUP (Spring-Summer 2027)
