TenetBench tests language models against professional ethics codes
Original title: TenetBench: A Clause-Anchored Russian Corpus for Evaluating Language Models against Professional Codes of Ethics
Eleven of fourteen model and reasoning-mode combinations produced conflicting content on most requests, usually without warning.
TenetBench pairs 3,433 requests that conflict with clauses from fifteen Russian-language professional codes with benign twins that differ only in legitimacy. A pipeline also produced tasks and human-validated verdicts from two English codes of other jurisdictions. Specialists rated 0.995 of the conflicting tasks plausible, and a model judge matched adjudicated labels at Gwet's AC1 of at least 0.894 on 1,860 elements. Enabling reasoning halved refusals for two models.
Unconfirmed: The abstract does not name the fourteen model and reasoning-mode combinations.
- Nearly three warnings in four accompany conflicting content rather than a clean refusal.
- Models that conform more decline the conflicting ask while still answering the legitimate twin.
- The frozen pipeline was human-validated, and the corpus can grow as codes are added.
From the original article
1 min read at huggingface.co
Codes of professional ethics guide and restrict specialists, who increasingly work with assistants built on large language models. Such an assistant can breach the user's code, or help the user breach it, without the user noticing. Evaluations reporting broad categories of harm cannot show how often this happens or which provision is breached.
By Yegor Anichkov, Valery Vinogradov, Andrey Furov · huggingface.co
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