Unleashing the Power of Language: A Performance-based Case Study of Large Language Models
Author(s):
Manikandan, Dr. BVANSS Prabhakar Rao
Keywords:
Large Language Models, language understanding, language generation, benchmark datasets, sentiment analysis, named entity recognition, text classification, dialogue generation, story completion, ethical considerations, biases, fairness, privacy concerns, future developments.
Abstract
Large Language Models (LLMs) have revolutionized the field of natural language processing (NLP) with their exceptional language understanding and generation capabilities. This research paper presents a comprehensive performance-based case study of LLMs to assess their effectiveness across various domains, identify their strengths and limitations, and explore the implications for future developments in NLP. The study includes an examination of LLMs' performance on benchmark datasets, encompassing natural language understanding (NLU) tasks such as sentiment analysis, named entity recognition, and text classification, as well as natural language generation (NLG) tasks like text summarization, dialogue generation, and story completion. Evaluation metrics such as accuracy, precision, recall, F1 score, and perplexity are employed to measure their performance. Additionally, the paper discusses the ethical considerations associated with LLMs, including biases, fairness, and privacy concerns, and explores potential challenges in deploying and utilizing these models effectively. By addressing these concerns and leveraging the potential of LLMs, this research aims to contribute to advancements in NLP and open up new possibilities across diverse domains.
Article Details
Unique Paper ID: 161549

Publication Volume & Issue: Volume 10, Issue 4

Page(s): 592 - 600
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