arXiv cs.CY· Zihan Chen, Lei Nico Zheng, Di Zhu·· 3 天前AI 评分58
研究：AI 没变笨，是人类评判标准在提高
AI Isn't Getting Dumber, We're Getting Pickier: Rising Standards in Human Evaluation of Large Language Models
AI 导读
一项基于 Chatbot Arena 2023 年 5 月至 2024 年 8 月 1,260,967 条盲投的研究发现，对同一对版本固定的模型，认为“两者都差”的投票占比平均每月上升 1.71 个百分点（均值 17.1%），2024 年为每月 1.91 个百分点。
正文 · 原文
Abstract:Users often complain that AI assistants such as ChatGPT are getting worse. Some versions do change, but human evaluation leaves a second possibility untested: the people judging may change. We separate the two on Chatbot Arena, a public platform where people compare answers from two anonymous large language models (LLMs) and pick the better one or say both are bad. We study 1,260,967 blind votes cast from May 2023 to August 2024 on pairs of models whose versions are fixed: dated vendor snapshots and models with fixed open weights. For the same pair of unchanged models, the share of votes saying "both are bad" rises by 1.71 percentage points a month on a mean of 17.1%, and by 1.91 points a month in 2024. In fourteen pairs observed often in both 2023 and 2024, it rose in every one, from 15.5% to 23.3%. The ranking does not trend: in 2024, the stronger model's share of decisive votes is flat. The rise holds within the same voter and on the same pair, and across arrival cohorts from their first recorded vote. It holds with prompt controls, in every major language, for dated vendor snapshots, open-weight models and models with stable answer length, and between interface changes. The drift is shared across models: month effects estimated from other models' pairs remove it from each model's own pairs. Platform-wide, the average model would be expected to score 78% against the 2023 average, yet the share of "both are bad" votes ended near where it began. The pattern fits a standard that follows the category's progress, so a product that stands still is judged as declining. Absolute verdicts on AI drifted while relative comparisons held steady, an evaluation drift that matters for LLM leaderboards and for the human feedback used to train models.
| Comments: | 23 pages, 6 figures, 10 tables; working paper |
| Subjects: | Human-Computer Interaction (cs.HC); Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Computers and Society (cs.CY) |
| ACM classes: | H.1.2; I.2.7; J.4 |
| Cite as: | arXiv:2611.14333 [cs.HC] |
| (or arXiv:2611.14333v1 [cs.HC] for this version) | |
| https://doi.org/10.48550/arXiv.2611.14333 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Di Zhu [view email]
[v1]
Tue, 10 Nov 2026 16:56:16 UTC (128 KB)
来源：arXiv cs.CY · arxiv.org