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dc.contributor.author Addala, Krishnasai
dc.contributor.author Baghel, Kabir Dev Paul
dc.contributor.author Shah, Rajiv Ratn (Advisor)
dc.date.accessioned 2024-05-27T05:40:49Z
dc.date.available 2024-05-27T05:40:49Z
dc.date.issued 2023-11-27
dc.identifier.uri http://repository.iiitd.edu.in/xmlui/handle/123456789/1616
dc.description.abstract Despite the growing capabilities of Large Language Models (LLMs) in various domains, their proficiency in addressing domain-specific high-school physics questions remains an unexplored area. In this study, we present a pioneering data set curated from NCERT exemplar solutions strategically designed to facilitate the use of LLMs to solve school physics questions. Originally comprising 766 questions accompanied by LaTeX representations, the dataset underwent a sophisticated augmentation process that expanded its scope to an impressive 7,983 questions. The augmentation employed innovative techniques which effectively broaden the dataset’s coverage. The dataset, prioritizing text-based questions, is formatted as JSON objects detailing instructions, inputs, and outputs. Post evaluation, we noted significant scores: METEOR at 0.282 and BERTScore F1 at 0.833, indicating a close alignment between generated and reference texts. en_US
dc.language.iso en_US en_US
dc.publisher IIIT-Delhi en_US
dc.subject Large Language Models en_US
dc.subject High School Education en_US
dc.subject Dataset en_US
dc.subject Chain of Thought en_US
dc.subject Artificial Intelligent en_US
dc.title AI based NLP systems en_US
dc.type Other en_US


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