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AYN: A Tiny Yet Competitive Indian Legal Language Model Pretrained from Scratch

Summary

Large decoder-only LLMs are expensive to train and run. They also learn mostly from Western legal material, so they fit Indian jurisprudence poorly, with its archaic vocabulary, dense citation patterns and code-switching. The authors test whether a tiny language model (under 100M parameters), trained from scratch on modest domain data, can replace much bigger general-purpose models. They build a 142.6M-word Indian legal corpus by extending ILDC with recent Supreme Court judgments, the Constitution and the Penal Code. They then train a dedicated BPE tokenizer and pretrain AYN, an 88M-parameter LLaMA-style decoder, on one A100 GPU. A position-index interpolation technique gives the model an 8192-token context despite limited memory. AYN is compared with LLMs of 1B–8B parameters. It beats models up to 80 times its size on legal case judgment prediction, nearly matches models up to 30 times larger on summarization, and remains competitive on general benchmarks. The results suggest that compact, domain-focused models offer an affordable path to legal NLP for under-represented jurisdictions.

Key points

  • Pretraining corpus (142,592,003 words):
    • 34,816 Supreme Court of India cases from ILDC (1947 – April 2020).
    • 3,046 newly collected cases (May 2020 – Dec 2023) from indiankanoon.org.
    • India's Constitution and its Penal Code, with text pulled from PDFs using tesseract.
  • Tokenizer: a legal-specific BPE tokenizer trained with SentencePiece. It aims to encode legal terms, multi-word expressions and citations more efficiently than general-purpose tokenizers.
  • Architecture:
    • 12 layers, hidden size 768, feed-forward size 2048.
    • RMSNorm, SwiGLU, no biases in dense layers, and weight tying.
    • RoPE with a position shrinking factor of 32, which yields an 8192-token context.
  • Training cost: about 185 hours on a single A100 GPU.
  • Instruction tuning (AYN-instruct): full fine-tuning on 10,763 deduplicated legal instructions. GPT-3.5-Turbo graded responses on Supreme Court-related tasks at roughly 8 out of 10 or higher on all four criteria (clarity, relevance, completeness, legal reasoning). Contract and clause tasks scored about 6.75–7.75.

Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.

Fields: Linguistics & languages · Law

Cite

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.63317/3c62f5f9g7q2

@inproceedings{niyogi2026ayn,
  title = {{AYN: A Tiny Yet Competitive Indian Legal Language Model Pretrained from Scratch}},
  author = {Niyogi, Mitodru and Gaussier, Eric and Bhattacharya, Arnab},
  booktitle = {Proceedings of the Fifteenth Language Resources and Evaluation Conference},
  year = {2026},
  doi = {10.63317/3c62f5f9g7q2},
  url = {https://doi.org/10.63317/3c62f5f9g7q2}
}

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