Leveraging LLaMA2 for improved document classification in English
Year: 2025
Type: article
Abstract: Document classification is an important component of natural language processing, with applications that include sentiment analysis, content recommendation, and information retrieval. This article investigates the potential of Large Language Model Meta AI (LLaMA2), a cutting-edge language model, to enhance document classification in English. Our experiments show that LLaMA2 outperforms traditional classification methods, achieving higher precision and recall values on the WOS-5736 dataset. Additionally, we analyze the interpretability of LLaMA2's classification process to reveal the most pertinent features for categorization and the model's decision-making. These results emphasize the potential of advanced language models to enhance classification outcomes and provide a more profound comprehension of document structures, thereby contributing to the advancement of natural language processing methodologies.
Source: PeerJ Computer Science
Author: Jia Xu
Institutions: Taizhou University, Taizhou University
Language: English
Topic: Topic Modeling
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $1,395
OpenAlex ID: W4408037575