{"id":12340,"date":"2024-06-28T09:00:47","date_gmt":"2024-06-28T09:00:47","guid":{"rendered":"https:\/\/whiteriversmediasolutions.com\/Sony\/women-in-tech-transitioning-careers-from-teaching-to-thriving-at-sony-research-india-copy\/"},"modified":"2024-07-10T05:24:49","modified_gmt":"2024-07-10T05:24:49","slug":"efficacy-of-large-language-models-in-predicting-hindi-movies-attributes-a-comprehensive-survey-and-content-based-analysis-summarized","status":"publish","type":"post","link":"https:\/\/whiteriversmediasolutions.com\/Sony\/efficacy-of-large-language-models-in-predicting-hindi-movies-attributes-a-comprehensive-survey-and-content-based-analysis-summarized\/","title":{"rendered":"\u2018Efficacy of Large Language Models in Predicting Hindi Movies’ Attributes: A Comprehensive Survey and Content-Based Analysis\u2019 \u2013 Summarized"},"content":{"rendered":"\t\t
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\u2018Efficacy of Large Language Models in Predicting Hindi Movies' Attributes: A Comprehensive Survey and Content-Based Analysis\u2019 \u2013 Summarized<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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\n\t\t\t\t\t\t\t\t\tSubham Raj (IIT Patna), Pawan Agrawal (IIT Patna), Sriparna Saha (IIT Patna), Brijraj Singh, Niranjan Pedanekar\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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\n\t\t\t\t\t\t\t\t\tThis article summarizes the paper titled \u2018Efficacy of Large Language Models in Predicting Hindi Movies’ Attributes: A Comprehensive Survey and Content-Based Analysis<\/a>\u2019 co-authored by Prabir Mondal, Professor Sriparna and team from IIT Patna and Brijraj Singh and Niranjan Pedanekar from Sony Research India. This paper has been accepted at The Web Conference 2024 (WWW 2024) | May 13-17, 2024<\/a>.\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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\n\t\t\t\t\t\t\t\t\tOur research delves into the effectiveness of four advanced Large Language Models (LLMs): GPT-3.5-turbo-0301, Vicuna, PaLM 2, and Dolly in predicting movie genres and meta-information like directors and cast details for Hindi movies. Traditional Deep Learning models for Recommendation Systems (RS) often require extensive data, but LLMs show promise in mitigating this need. The study focuses on Hindi movies from the Flickscore dataset, particularly analyzing audio transcripts from movie trailers.\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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\n\t\t\t\t\t\t\t\t\tPreliminary findings highlight GPT-3.5 as the most proficient LLM in predicting movie meta-information accurately. Despite the complexities involved in predicting diverse aspects like genres and user preferences, GPT-3.5 exhibits promising capabilities. This research contributes to advancing our understanding of LLMs in movie-related tasks and their potential application in RS, indicating progress in user preference comprehension and personalized content recommendations.\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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\n\t\t\t\t\t\t\t\t\tThe experiments utilize audio information from 510 Hindi movie trailers in the Flickscore dataset, covering various user-movie interactions and preferences. The dataset comprises of movie names, directors, casts, year of release, storylines, and multiple genres. Transcripts of the Hindi audio are translated into English for analysis, forming the corpus for the experiment.\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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\n\t\t\t\t\t\t\t\t\tKey research contributions include a) Analyzing the effect of LLMs in predicting Hindi movie genres and meta-information from audio transcripts. b) How well LLMs recall directors and cast based on movie names, year of release, and c) The proficiency of GPT-3.5 in predicting meta-information for newer Hindi movies.\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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\n\t\t\t\t\t\t\t\t\tThe study’s conclusion highlights GPT-3.5’s superior performance in predicting movie metadata and introduces a decision-making RS utilizing LLM responses for user-movie pair preferences, competing effectively with existing systems.\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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To know more about Sony Research India\u2019s Research Publications, visit the \u2018Publications\u2019 section on our \u2018Open Innovation\u2019s page: Open Innovation with Sony R&D \u2013 Sony Research India<\/a><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t

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\n\t\t\t\t\t\t\t\t\tThe introduced modules and techniques help the proposed method to align known class\nrepresentations effectively so that it can detect the unknown objects accurately. To validate\nthis, we carried out extensive experiments & ablation studies and found that the proposed\nmethod outperforms existing SOTA methods with significant improvement on the MS-COCO\n& PASCAL VOC dataset for the OSOD task.\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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\n\t\t\t\t\t\t\t\t\tTo know more about the paper, visit: Open-Set Object Detection by Aligning Known Class\nRepresentations (thecvf.com)<\/a>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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\n\t\t\t\t\t\t\t\t\tTo know more about Sony Research India\u2019s Research Publications, visit the \u2018Publications\u2019\nsection on our \u2018Open Innovation\u2019s page: Open Innovation with Sony R&D \u2013 Sony Research\nIndia<\/a>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<\/div>\n\t\t","protected":false},"excerpt":{"rendered":"

This article summarizes the paper titled \u2018Efficacy of Large Language Models… <\/p>\n","protected":false},"author":1,"featured_media":12343,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"elementor_header_footer","format":"standard","meta":{"footnotes":""},"categories":[22,17],"tags":[],"class_list":["post-12340","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-all-blogs","category-technology","entry"],"yoast_head":"\n\u2018Efficacy of Large Language Models in Predicting Hindi Movies' Attributes: A Comprehensive Survey and Content-Based Analysis\u2019 \u2013 Summarized - Sony Research India<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" 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