Leveraging LLaMA2 for improved document classification in English.

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Tác giả: Jia Xu

Ngôn ngữ: eng

Ký hiệu phân loại: 615.399 Blood substitutes

Thông tin xuất bản: United States : PeerJ. Computer science , 2025

Mô tả vật lý:

Bộ sưu tập: NCBI

ID: 684395

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.
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