README.md

metadata

dataset_info:
  - config_name: imaginary-reference
    features:
      - name: role
        dtype: string
      - name: content
        dtype: string
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      - name: test
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  - config_name: math
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      - name: content
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      - name: content
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  - config_name: unanswerable
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      - name: content
        dtype: string
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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

Citation

@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}, 
}