---
dataset_info:
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configs:
- config_name: imaginary-reference
  data_files:
  - split: test
    path: imaginary-reference/test-*
- config_name: indifferent
  data_files:
  - split: test
    path: indifferent/test-*
- config_name: math
  data_files:
  - split: test
    path: math/test-*
- config_name: redundant
  data_files:
  - split: test
    path: redundant/test-*
- config_name: unanswerable
  data_files:
  - split: test
    path: unanswerable/test-*
license: cc-by-nc-4.0
language:
- en
---
# 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}, 
}
```