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Language Decoded | Multilingual Code Dataset

Multilingual Python code datasets for the Language Decoded project (part of Cohere's Tiny Aya Expedition), investigating whether code's reasoning benefit for language models is language-dependent or structure-dependent.

Research Question

Does fine-tuning on non-English code (Python with translated keywords) improve multilingual reasoning as much as English code does?

Prior work (Aryabumi et al., 2024) showed English code improves English reasoning by 8.2%, but never tested non-English code. This dataset enables that experiment.

Dataset Structure

This repo contains multiple experimental conditions as subdirectories:

Subdirectory	Condition	Description
`source-python/`	Source	Filtered Python files from The Stack (shared base)
`baseline/`	Condition 1	No code augmentation (control)
`english-code/`	Condition 2	Original English-keyword Python code
`multilingual-code-ur/`	Condition 3a	Python transpiled to Urdu keywords via Legesher
`multilingual-code-am/`	Condition 3b	Python transpiled to Amharic keywords via Legesher
`multilingual-code-zh/`	Condition 3c	Python transpiled to Chinese keywords via Legesher
`multilingual-text/`	Condition 4	Non-code multilingual text (control)

Usage

from datasets import load_dataset

# Load a specific condition
ds = load_dataset("Legesher/language-decoded-data", data_dir="multilingual-code-ur")

Transpilation

Code translation is performed using Legesher, which translates Python reserved words (keywords, builtins, exceptions) into target languages while preserving code structure and semantics.

Example (English → Chinese):

# English
for item in range(10):
    if item > 5:
        print(item)

# Chinese / 中文 (via Legesher)
循环 元素 在 范围(10):
    如果 元素 > 5:
        打印(元素)

Source Data

Base: The Stack — permissively licensed Python subset
Filtering: Quality-filtered to 50K-100K files
Transpilation tool: Legesher v0.6.0+

Evaluation Benchmarks

Models fine-tuned on these conditions are evaluated on:

XNLI — Cross-lingual natural language inference (15 languages)
XStoryCloze — Story completion (11 languages)
TyDi QA — Question answering (11 languages)
MMLU — Multilingual knowledge

Related Resources

Models: Legesher/language-decoded-lora — LoRA adapters trained on these conditions
Community code: Legesher/language-decoded-community — Human-written native language code
Experiments: Legesher/language-decoded-experiments — Training logs and eval results
Paper: Coming soon

Citation

@misc{language-decoded-2026,
  title={Language Decoded: Investigating Language-Dependent vs. Structure-Dependent Reasoning Benefits of Code},
  author={Madison Edgar and Saad Bazaz and Rafay Mustafa and Sarah Jawaid and Rashik Shahjahan and Khojasteh Mirza and Sohaib Bazaz},
  year={2026},
  publisher={Hugging Face},
  url={https://huggingface.co/datasets/Legesher/language-decoded-data}
}

License

Apache 2.0

Downloads last month: 4

Paper for legesher/language-decoded-data

To Code, or Not To Code? Exploring Impact of Code in Pre-training

Paper • 2408.10914 • Published Aug 20, 2024 • 45