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Small LLMs improved for legal QA with context-injected fine-tuning

Researchers have developed a method called context-injected fine-tuning to improve the legal question-answering capabilities of small language models. They curated a dataset of 2,165 bilingual legal records from Bangladesh and fine-tuned Qwen3.5 models of varying sizes. The fine-tuning significantly improved the models' ability to use provided legal statutes and answer in the requested language, particularly for smaller model scales. AI

IMPACT This research demonstrates a method to enhance the legal reasoning and language adherence of smaller AI models, potentially making specialized legal AI more accessible.

RANK_REASON The cluster contains an academic paper detailing a new method for fine-tuning language models. [lever_c_demoted from research: ic=1 ai=1.0]

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Small LLMs improved for legal QA with context-injected fine-tuning

COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Moniruzzaman Mahadi, Abrar Mohammed Tanzim Alam, Sayma Siddika Monalisa, Mir Mohammad Asif Abdullah, Swakkhar Shatabda, Md Adnan Arefeen ·

    Do Small Models Use the Law You Give Them? Context-Injected Fine-Tuning for Legal QA in Bangladesh

    arXiv:2607.23446v1 Announce Type: cross Abstract: A small language model can receive the governing statutory provision and still answer incorrectly. We test whether fine-tuning on examples containing relevant law improves later use of retrieved law. We curate 2{,}165 bilingual QA…