Summary of Suvach — Generated Hindi Qa Benchmark, by Vaishak Narayanan et al.
Suvach – Generated Hindi QA benchmark
by Vaishak Narayanan, Prabin Raj KP, Saifudheen Nouphal
First submitted to arxiv on: 30 Apr 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper proposes a novel benchmark for evaluating question answering (QA) models in Indic languages, specifically designed for Hindi. The current approach relies on machine translation of English datasets, which introduces biases and inaccuracies. To address this, the authors leverage large language models to generate a high-quality dataset in an extractive setting, ensuring its relevance for Hindi. This new resource aims to foster advancements in Hindi NLP research by providing a more accurate and reliable evaluation tool. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper creates a special test for computers that answer questions in Hindi. Right now, people use English tests and translate them into Hindi. But this can be wrong because it doesn’t understand Hindi language very well. To fix this, the authors make a new test using big computer models to create high-quality questions and answers in Hindi. This will help improve Hindi language research by giving scientists a better way to check how good their ideas are. |
Keywords
» Artificial intelligence » Nlp » Question answering » Translation
