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license: cc-by-nc-4.0
language:
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---
# DNR Bench
Don’t Reason Bench (DNR Bench), a novel benchmark designed to expose a vulnerability in current RLMs: their tendency to over-reason by attempting to solve unsolvable
problems, leading to excessively long responses.
# Data Summary
The DNR Bench dataset contains 150 adversarially crafted prompts divided into five distinct categories:
- Imaginary Reference
- Indifferent
- Math,
- Redundant,
- Unanswerable.
Each category targets a specific failure mode observed in reasoning-optimized LLMs, such as hallucinating nonexistent references, failing to remain neutral in ambiguous contexts, incorrectly solving flawed math problems, overanalyzing redundant information, or answering questions that lack sufficient data.
# Leaderboard
This dataset is used to test reasoning LLMs in [DNR Leaderboard on Huggingface](https://huggingface.co/spaces/ServiceNow-AI/Do-not-reason-bench)
# Citation
```bibtex
@misc{hashemi2025dnrbenchbenchmarkingoverreasoning,
title={DNR Bench: Benchmarking Over-Reasoning in Reasoning LLMs},
author={Masoud Hashemi and Oluwanifemi Bamgbose and Sathwik Tejaswi Madhusudhan and Jishnu Sethumadhavan Nair and Aman Tiwari and Vikas Yadav},
year={2025},
eprint={2503.15793},
archivePrefix={arXiv},
primaryClass={cs.LG},
url={https://arxiv.org/abs/2503.15793},
}
``` |