---
license: cc-by-nc-nd-4.0
task_categories:
- question-answering
tags:
- reasoning
- linguistics
- benchmark
pretty_name: L2
size_categories:
- 1K<n<10K
source_datasets:
- https://huggingface.co/datasets/ambean/lingOly
configs:
  - config_name: default
    data_files:
      - split: test
        path: test_small.zip
extra_gated_prompt: >-
  ### LingOly-TOO LICENSE AGREEMENT

  This dataset is governed by a CC-BY-NC-ND-4.0 license. 

  In addition to this license, we ask that uses of the dataset are in line with
  the Acceptable Use policy described below.

  ### Acceptable Use Policy

  This dataset is intended as a benchmark for reasoning in large language
  models. For the integrity of the benchmark, users should not:
      * Re-distribute the questions or answers of the benchmark in formats (such as plain text) which leak the benchmark to web-scraping.
      * Train language models directly using the content of this benchmark.
extra_gated_fields:
  By clicking Submit below I accept the terms of the license and Acceptable Use policy: checkbox
extra_gated_button_content: Submit
---
<div align="center">
  <center><h1>LingOly-TOO (L2)</h1></center>
</div>

![alt text](main_scores.svg)

## Links
 - 📊 [Website and Leaderboard](https://huggingface.co/spaces/jkhouja/lingoly-too)
 - 📎 [Paper](https://arxiv.org/abs/2503.02972)
 - 🧩 [Code](https://github.com/jkhouja/L2)

## Summary
LingOly-TOO (L2) is a challenging linguistics reasoning benchmark designed to counteracts answering without reasoning (e.g. by guessing or memorizing answers).

## Dataset format
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.

```python
{'question_n':            # The question number in the problem
 'prompt':                # The main text of the question including preamble, context and previous questions
 'completion':            # The correct answer
 'question':              # The question text only (without the the rest of the prompt)
 'context':               # Context text that includes important information, you should prepend your prompt with context for solvability
 'obfuscated':            # If this example was obfuscated or not
 'overall_question_n':    # The problem number
 'obfuscated_question_n': # Concatenation of problem number and obfuscation number
}
```

## Citation
```
@article{khouja2025lingolytoodisentanglingmemorisationreasoning,
      title={LINGOLY-TOO: Disentangling Memorisation from Reasoning with Linguistic Templatisation and Orthographic Obfuscation}, 
      author={Jude Khouja and Karolina Korgul and Simi Hellsten and Lingyi Yang and Vlad Neacsu and Harry Mayne and Ryan Kearns and Andrew Bean and Adam Mahdi},
      year={2025},
      eprint={2503.02972},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2503.02972}, 
}

```