{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,28]],"date-time":"2026-03-28T18:11:07Z","timestamp":1774721467992,"version":"3.50.1"},"publisher-location":"Cham","reference-count":29,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031496004","type":"print"},{"value":"9783031496011","type":"electronic"}],"license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023]]},"DOI":"10.1007\/978-3-031-49601-1_5","type":"book-chapter","created":{"date-parts":[[2023,12,4]],"date-time":"2023-12-04T00:02:07Z","timestamp":1701648127000},"page":"64-79","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Revolutionizing High School Physics Education: A Novel Dataset"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0009-0003-2479-0342","authenticated-orcid":false,"given":"Avinash","family":"Anand","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-9962-1531","authenticated-orcid":false,"given":"Krishnasai","family":"Addala","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-4428-4818","authenticated-orcid":false,"given":"Kabir","family":"Baghel","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-8508-0517","authenticated-orcid":false,"given":"Arnav","family":"Goel","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-7477-3064","authenticated-orcid":false,"given":"Medha","family":"Hira","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-1399-8262","authenticated-orcid":false,"given":"Rushali","family":"Gupta","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1028-9373","authenticated-orcid":false,"given":"Rajiv Ratn","family":"Shah","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,12,4]]},"reference":[{"key":"5_CR1","unstructured":"Zhang, Z., Zhang, A., Li, M., Smola, A.: Automatic Chain of Thought Prompting in Large Language Models (2022)"},{"key":"5_CR2","doi-asserted-by":"publisher","unstructured":"Kojima, T., Gu, S.S., Reid, M., Matsuo, Y., Iwasawa, Y.: Large language models are zero-shot reasoners (2023). https:\/\/doi.org\/10.48550\/arXiv.2205.11916","DOI":"10.48550\/arXiv.2205.11916"},{"key":"5_CR3","doi-asserted-by":"publisher","unstructured":"Wei, J., et al.: Chain-of-thought prompting elicits reasoning in large language models (2023). https:\/\/doi.org\/10.48550\/arXiv.2201.11903","DOI":"10.48550\/arXiv.2201.11903"},{"key":"5_CR4","doi-asserted-by":"publisher","unstructured":"Bryant, S.: Assessing GPT-4\u2019s Role as a Co-Collaborator in Scientific Research: A Case Study Analyzing Einstein\u2019s Special Theory of Relativity (2023). https:\/\/doi.org\/10.21203\/rs.3.rs-2808494\/v2","DOI":"10.21203\/rs.3.rs-2808494\/v2"},{"key":"5_CR5","doi-asserted-by":"publisher","unstructured":"Zhou, C., et al.: LIMA: less is more for alignment (2023). https:\/\/doi.org\/10.48550\/arXiv.2305.112062","DOI":"10.48550\/arXiv.2305.112062"},{"key":"5_CR6","doi-asserted-by":"publisher","unstructured":"Lightman, H., et al.: Let\u2019s verify step by step (2023). https:\/\/doi.org\/10.48550\/arXiv.2305.20050","DOI":"10.48550\/arXiv.2305.20050"},{"key":"5_CR7","unstructured":"Gunasekar, S., et al.: Textbooks Are All You Need (2023). https:\/\/doi.org\/qaojh1JD"},{"key":"5_CR8","doi-asserted-by":"publisher","unstructured":"Mandlecha, P., Chatakonda, S.K., Kollepara, N., Kumar, P.: Hybrid Tokenization and Datasets for Solving Mathematics and Science Problems Using Transformers (2023). https:\/\/doi.org\/10.1137\/1.9781611977172.33","DOI":"10.1137\/1.9781611977172.33"},{"key":"5_CR9","doi-asserted-by":"publisher","unstructured":"Cobbe, K., et al.: Training verifiers to solve math word problems (2023). https:\/\/doi.org\/10.48550\/arXiv.2110.14168","DOI":"10.48550\/arXiv.2110.14168"},{"key":"5_CR10","doi-asserted-by":"publisher","unstructured":"Arora, D., Singh, H.G.: Mausam: have LLMs advanced enough? A challenging problem solving benchmark for large language models (2023). https:\/\/doi.org\/10.48550\/arXiv.2305.15074","DOI":"10.48550\/arXiv.2305.15074"},{"key":"5_CR11","doi-asserted-by":"publisher","unstructured":"Hu, E.J., et al.: LoRA: low-rank adaptation of large language models (2021). https:\/\/doi.org\/10.48550\/arXiv.2106.09685","DOI":"10.48550\/arXiv.2106.09685"},{"key":"5_CR12","doi-asserted-by":"publisher","unstructured":"Dettmers, T., Pagnoni, A., Holtzman, A., Zettlemoyer, L.: QLoRA: efficient finetuning of quantized LLMs (2023). https:\/\/doi.org\/10.48550\/arXiv.2305.14314","DOI":"10.48550\/arXiv.2305.14314"},{"key":"5_CR13","doi-asserted-by":"publisher","unstructured":"Zhao, T.Z., Wallace, E., Feng, S., Klein, D., Singh, S.: Calibrate before use: improving few-shot performance of language models (2021). https:\/\/doi.org\/10.48550\/arXiv.2102.09690","DOI":"10.48550\/arXiv.2102.09690"},{"key":"5_CR14","doi-asserted-by":"publisher","unstructured":"Liu, J., Shen, D., Zhang, Y., Dolan, B., Carin, L., Chen, W.: What Makes good in-context examples for GPT-3? (2021). https:\/\/doi.org\/10.48550\/arXiv:2101.06804","DOI":"10.48550\/arXiv:2101.06804"},{"key":"5_CR15","doi-asserted-by":"publisher","unstructured":"Ling, W., Yogatama, D., Dyer, C., Blunsom, P.: Program induction by rationale generation: learning to solve and explain algebraic word problems (2017). https:\/\/doi.org\/10.48550\/arXiv.1705.04146","DOI":"10.48550\/arXiv.1705.04146"},{"key":"5_CR16","doi-asserted-by":"publisher","unstructured":"Chen, J., et al.: GeoQA: a geometric question answering benchmark towards multimodal numerical reasoning (2022). https:\/\/doi.org\/10.48550\/arXiv.2105.14517","DOI":"10.48550\/arXiv.2105.14517"},{"key":"5_CR17","doi-asserted-by":"publisher","unstructured":"Welbl, J., Liu, N.F., Gardner, M.: Crowdsourcing multiple choice science questions (2017). https:\/\/doi.org\/10.48550\/arXiv.1707.06209","DOI":"10.48550\/arXiv.1707.06209"},{"key":"5_CR18","doi-asserted-by":"publisher","unstructured":"Azaria, A., Mitchell, T.: The internal state of an LLM knows when its lying (2023). https:\/\/doi.org\/10.48550\/arXiv.2304.13734","DOI":"10.48550\/arXiv.2304.13734"},{"key":"5_CR19","doi-asserted-by":"publisher","unstructured":"Thorne, J., Vlachos, A., Christodoulopoulos, C., Mittal, A.: FEVER: a large-scale dataset for Fact Extraction and VERification (2018). https:\/\/doi.org\/10.48550\/arXiv.1803.05355","DOI":"10.48550\/arXiv.1803.05355"},{"key":"5_CR20","doi-asserted-by":"publisher","unstructured":"Yao, S., et al.: Tree of thoughts: deliberate problem solving with large language models (2023). https:\/\/doi.org\/10.48550\/arXiv.2305.10601","DOI":"10.48550\/arXiv.2305.10601"},{"key":"5_CR21","doi-asserted-by":"publisher","unstructured":"Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: evaluating text generation with BERT (2020). https:\/\/doi.org\/10.48550\/arXiv.1904.09675","DOI":"10.48550\/arXiv.1904.09675"},{"key":"5_CR22","unstructured":"Banerjee, S., Lavie, A.: METEOR: an 476 automatic metric for MT evaluation with improved correlation relation with human judgments. In: Proceedings of the ACL Workshop on Intrinsic and Extrinsic Evaluation Measures for Machine Translation and\/or Summarization, pp. 65\u201372 (2005)"},{"key":"5_CR23","unstructured":"Lin, C.-Y.: ROUGE: a package for automatic evaluation of summaries. Text summarization branches out, pp. 74\u201381 (2004)"},{"key":"5_CR24","doi-asserted-by":"publisher","unstructured":"Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311\u2013318 (2002). https:\/\/doi.org\/10.3115\/1073083.1073135","DOI":"10.3115\/1073083.1073135"},{"key":"5_CR25","doi-asserted-by":"publisher","unstructured":"Wang, Y., et al.: Self-instruct: aligning language models with self-generated instructions (2023). https:\/\/doi.org\/10.48550\/arXiv.2212.10560","DOI":"10.48550\/arXiv.2212.10560"},{"key":"5_CR26","doi-asserted-by":"publisher","unstructured":"Touvron, H., Martin, L., Stone, K., Albert, P., et al.: Llama 2: open foundation and fine-tuned chat models (2023). https:\/\/doi.org\/10.48550\/arXiv.2307.09288","DOI":"10.48550\/arXiv.2307.09288"},{"key":"5_CR27","doi-asserted-by":"publisher","unstructured":"Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding (2019). https:\/\/doi.org\/10.48550\/arXiv.1810.04805","DOI":"10.48550\/arXiv.1810.04805"},{"key":"5_CR28","doi-asserted-by":"publisher","unstructured":"Vaswani, A., et al.: Attention is all you need. Adv. Neural Inf. Process. Syst. 30 (2017). https:\/\/doi.org\/10.48550\/arXiv.1706.03762","DOI":"10.48550\/arXiv.1706.03762"},{"key":"5_CR29","first-page":"2579","volume":"9","author":"L van der Maaten","year":"2008","unstructured":"van der Maaten, L., Hinton, G.: Visualizing data using t-SNE. J. Mach. Learn. Res. 9, 2579\u20132605 (2008)","journal-title":"J. Mach. Learn. Res."}],"container-title":["Lecture Notes in Computer Science","Big Data and Artificial Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-49601-1_5","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,12,4]],"date-time":"2023-12-04T00:02:36Z","timestamp":1701648156000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-49601-1_5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031496004","9783031496011"],"references-count":29,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-49601-1_5","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"4 December 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"BDA","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Big Data Analytics","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Delhi","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"India","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"7 December 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"9 December 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"11","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"bigda2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/bda23.iiitd.edu.in\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"CMT, Microsoft","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"67","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"17","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"25% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"4","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}