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README.md
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## Links
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- 📊 [Website and Leaderboard](https://huggingface.co/spaces/jkhouja/lingoly-too)
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- Paper
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- 🧩 [Repo](https://github.com/jkhouja/L2)
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## Summary
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LingOly-TOO (L2) is a challenging reasoning benchmark designed to
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## Dataset format
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LingOly-TOO benchmark was created by generating up to 6 obfuscations per problem for 82 problems source from original LingOly benchmark.
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```python
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{'question_n': # The question number in the problem
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## Citation
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```
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@article{
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title={LINGOLY-TOO: Disentangling Memorisation from Reasoning with Linguistic Templatisation and Orthographic Obfuscation},
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author={Khouja
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year={2025},
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eprint
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archivePrefix={arXiv},
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primaryClass={cs.CL},
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url
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}
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```
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## Links
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- 📊 [Website and Leaderboard](https://huggingface.co/spaces/jkhouja/lingoly-too)
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- 📎 [Paper](https://arxiv.org/abs/2503.02972)
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- 🧩 [Repo](https://github.com/jkhouja/L2)
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## Summary
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LingOly-TOO (L2) is a challenging linguistics reasoning benchmark designed to counteracts answering without reasoning (e.g. by guessing or memorizing answers).
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## Dataset format
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LingOly-TOO benchmark was created by generating up to 6 obfuscations per problem for 82 problems source from original LingOly benchmark. Dataset contains over 1200 question answer pairs. Some answers consists of multiple parts.
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```python
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{'question_n': # The question number in the problem
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## Citation
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```
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@article{khouja2025lingolytoodisentanglingmemorisationreasoning,
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title={LINGOLY-TOO: Disentangling Memorisation from Reasoning with Linguistic Templatisation and Orthographic Obfuscation},
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author={Jude Khouja and Karolina Korgul and Simi Hellsten and Lingyi Yang and Vlad Neacs and Harry Mayne and Ryan Kearns and Andrew Bean and Adam Mahdi},
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year={2025},
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eprint={2503.02972},
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archivePrefix={arXiv},
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primaryClass={cs.CL},
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url={https://arxiv.org/abs/2503.02972},
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}
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```
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