arXiv cs.CL· Tapan Parikh·· 12 小时前AI 评分58
论文提出用 40 个单词答案识别 API 背后的语言模型
Forty Questions: Identifying a Language Model from One-Word Answers
AI 导读
Tapan Parikh 的论文提出一种方法，通过模型对“说出一个声音”“说出一个诗人”等日常问题的单词回答来识别 API 背后的模型。在 94 个模型的测试面板上，40 个答案有 92.5% 的概率精确识别出模型，随机猜测仅 1.1%。
正文 · 原文
Abstract:A model behind an API can be identified from its one-word answers to everyday questions such as "Name a sound" or "Name a poet". A model's profile records how often it gives each answer to each question. We attribute new answers to the model most likely to have given them. On a panel of 94 models, 40 answers identify the exact model 92.5% of the time, against 1.1% by chance. Profiles built through OpenRouter identified all ten models served by DeepInfra under blinded labels. A system prompt lowers accuracy by 6 to 16 points. We applied the method to 96 endpoints of 13 open models on OpenRouter, and 92 identify as the model they claim. Of the other four, one matches the claimed model with reasoning turned off. One serves Gemma 4 under the name Llama 4 Maverick: it names itself Gemma 4, and it is billed Gemma 4's token counts on every message. Two remain unresolved. Billing records also show context added to first-party models. Grok 4.7 is billed 1,247 input tokens for a one-line question that costs GPT-4o only 12 tokens, and five models state the current date without being billed for it. The questions come from a public census of model defaults, and the method costs cents per endpoint.
| Comments: | 19 pages. Code and data: this https URL |
| Subjects: | Computation and Language (cs.CL) |
| Cite as: | arXiv:2610.28281 [cs.CL] |
| (or arXiv:2610.28281v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2610.28281 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Tapan Parikh [view email]
[v1]
Tue, 20 Oct 2026 14:33:56 UTC (20 KB)
来源：arXiv cs.CL · arxiv.org