Efficacy of Large Language Models in Predicting Hindi Movies' Attributes: A Comprehensive Survey and Content-Based Analysis

Prabir Mondal, Siddharth Singh, Kushum, Sriparna Saha, Jyoti Prakash Singh, Brijraj Singh, Niranjan Pedanekar

Abstract: This research explores the efficacy of four state-of-the-art Large Language Models (LLMs): GPT-3.5-turbo-0301, Vicuna, PaLM 2, and Dolly in predicting (i) movie genres using audio transcripts of movie trailers and (ii) meta-information such as director and cast details using movie name and its year-of-release (YoR) for Hindi movies. In the contemporary landscape, training models for movie meta-information prediction often demand extensive data and parameters, posing significant challenges. We aim to discern whether LLMs mitigate these challenges. Focusing on Hindi movies within the Flickscore dataset, our study concentrates on trailer data. Preliminary findings reveal that GPT-3.5 stands out as the most effective LLM in predicting movie meta-information. Despite the inherent complexities of predicting diverse aspects such as genres and user preferences, GPT-3.5 exhibits promising capabilities. This research not only contributes to advancing our understanding of LLMs in the context of movie-related tasks but also sheds light on their potential application in Recommendation Systems (RS), indicating a notable leap forward in user preference comprehension and personalized content recommendations.