A Systematic Literature Review on LLM-Based Information Retrieval: The Issue of Contents Classification
Abstract
This paper conducts a systematic literature review on applying Large Language Models (LLMs) in information retrieval, specifically focusing on content classification. The review explores how LLMs, particularly those based on transformer architectures, have addressed long-standing challenges in text classification by leveraging their advanced context understanding and generative capabilities. Despite the rapid advancements, the review identifies gaps in current research, such as the need for improved transparency, reduced computational costs, and the handling of model hallucinations. The paper concludes with recommendations for future research directions to optimize the use of LLMs in content classification, ensuring their effective deployment across various domains.
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