Building legal QA systems for low-resource languages requires domain-specific benchmarks and models—simple retrieval methods fail dramatically (20% F1) compared to fine-tuned legal BERT (85% F1) on Indian IPR law questions.
This paper introduces IndicIPR-QA, a benchmark dataset of 4,462 question-answer pairs for extractive QA on Indian intellectual property law across seven statutory acts. The dataset was validated through automatic checks, LLM evaluation, and human review, with experiments showing that fine-tuned legal BERT models significantly outperform simple keyword matching on this task.