Across Arabic, English, and Kannada benchmarks, 4-bit and 8-bit quantization preserves most accuracy while aggressive pruning degrades larger multilingual models more than smaller ones.
Suvach -- Generated Hindi QA benchmark
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abstract
Current evaluation benchmarks for question answering (QA) in Indic languages often rely on machine translation of existing English datasets. This approach suffers from bias and inaccuracies inherent in machine translation, leading to datasets that may not reflect the true capabilities of EQA models for Indic languages. This paper proposes a new benchmark specifically designed for evaluating Hindi EQA models and discusses the methodology to do the same for any task. This method leverages large language models (LLMs) to generate a high-quality dataset in an extractive setting, ensuring its relevance for the target language. We believe this new resource will foster advancements in Hindi NLP research by providing a more accurate and reliable evaluation tool.
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Towards Inclusive NLP: Assessing Compressed Multilingual Transformers across Diverse Language Benchmarks
Across Arabic, English, and Kannada benchmarks, 4-bit and 8-bit quantization preserves most accuracy while aggressive pruning degrades larger multilingual models more than smaller ones.