Dataset · Hugging Face

pooria/benchmark-honesty

Name: benchmark-honesty
Creator: pooria
License: mit
Keywords: task_categories:text-generation, language:en, license:mit, size_categories:n<1K, format:json, modality:text, library:datasets, library:pandas, library:polars, library:mlcroissant, region:us, benchmark, llm-evaluation, ai-benchmark, model-evaluation

Honesty Admit uncertainty when retrieval cannot support an answer. What it measures Whether a model retries transient retrieval failures, answers from evidence, and abstains instead of fabricating unavailable facts. Category: safety. Status: alpha. How to run $ npx benchmark-honesty list $ npx benchmark-honesty run --model <model-id> --provider openrouter $ npx benchmark-honesty score results.json --provider mock runs the whole benchmark offline… See the full description on the dataset page: https://huggingface.co/datasets/pooria/benchmark-honesty.

View on Hugging FaceReport an issue

Author / organization	pooria
License (as declared)	mit
Downloads	0 (as reported by Hugging Face)
Likes	0
Created	16 October 2026
Last updated at source	16 October 2026

benchmarkllm-evaluationai-benchmarkmodel-evaluationagent-benchmarkhallucinationhonestyabstentioncalibrationbrier-scoretool-useopenrouteropenaimcp

Embed this entry

<iframe src="https://silytics.si/embed/item/83e77ce4-4316-46b3-862c-f57629c49aa4" width="420" height="190" style="border:0" loading="lazy" title="Silytics entry"></iframe>

Free, no tracking. More about feeds and embeds.

More from pooria

Silytics shows public metadata from Hugging Face and links to the original. Information may be incomplete or out of date. Last refreshed 16 October 2026. Licenses and terms of the original apply to the work itself. Not financial, legal or professional advice.