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Advancements and Applications of Large Language Models in Natural Language Processing: A Comprehensive Review
- 1 University of California, Irvine, Irvine, 92617, United States of America
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Abstract
Large language models (LLMs) have revolutionized the field of natural language processing (NLP), demonstrating remarkable capabilities in understanding, generating, and manipulating human language. This comprehensive review explores the development, applications, optimizations, and challenges of LLMs. This paper begin by tracing the evolution of these models and their foundational architectures, such as the Transformer, GPT, and BERT. We then delve into the applications of LLMs in natural language understanding tasks, including sentiment analysis, named entity recognition, question answering, and text summarization, highlighting real-world use cases. Next, we examine the role of LLMs in natural language generation, covering areas such as content creation, language translation, personalized recommendations, and automated responses. We further discuss LLM applications in other NLP tasks like text style transfer, text correction, and language model pre-training. Subsequently, we explore techniques for optimizing and improving LLMs, including model compression, explainability, robustness, and security. Finally, we address the challenges posed by the significant computational requirements, sample inefficiency, and ethical considerations surrounding LLMs. We conclude by discussing potential future research directions, such as efficient architectures, few-shot learning, bias mitigation, and privacy-preserving techniques, which will shape the ongoing development and responsible deployment of LLMs in NLP.
Keywords
Large Language Model, Natural Language Processing, Review, Transformer.
[1]. Vaswani, A., et al. (2017). Attention is All You Need. Neural Information Processing Systems.
[2]. Radford, A., et al. (2018). Improving Language Understanding by Generative Pre-training. OpenAI Blog.
[3]. Devlin, J., et al. (2018). BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. North American Chapter of the Association for Computational Linguistics.
[4]. Brown, T., et al. (2020). Language Models are Few-Shot Learners. Neural Information Processing Systems.
[5]. Rogers, A., et al. (2020). A Primer in BERTology: What we know about how BERT works. Transactional Association of Computational Linguistics.
[6]. Ruder, S., et al. (2019). Transfer Learning in Natural Language Processing. NAACL HLT 2019 Tutorial.
[7]. Wu, Yuting, Ziyu Wang, and Wei D. Lu. (2024) "PIM GPT a hybrid process in memory accelerator for autoregressive transformers." npj Unconventional Computing 1.1.
[8]. Liu, Y., et al. (2019). RoBERTa: A Robustly Optimized BERT Pretraining Approach. arXiv preprint arXiv:1907.11692.
[9]. Raffel, C., et al. (2019). Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer. Journal of Machine Learning Research.
[10]. Zhu, Y., et al. (2015). Aligning Books and Movies: Towards Story-like Visual Explanations by Watching Movies and Reading Books. ICCV.
[11]. Howard, J., and Ruder, S. (2018). Fine-tuned Language Models for Text Classification. arXiv preprint arXiv:1801.06146.
[12]. Rojas, Santiago. "Automating Customer Service with AI-Powered Large Language Models." Journal of Innovative Technologies 7.1 (2024).
[13]. Chen, Zhiyu Zoey, et al. "A Survey on Large Language Models for Critical Societal Domains: Finance, Healthcare, and Law." arXiv preprint arXiv:2405.01769 (2024).
[14]. Jiang, Gongyao, Xinran Shi, and Qiong Luo.(2024) "LLM-Collaboration on Automatic Science Journalism for the General Audience." arXiv preprint arXiv:2407.09756.
[15]. Weber, Christoph Johannes, Sebastian Burgkart, and Sylvia Rothe.(2024) "wr-AI-ter: Enhancing Ownership Perception in AI-Driven Script Writing." Proceedings of the 2024 ACM International Conference on Interactive Media Experiences.
[16]. Zafrir, O., Boudoukh, G., Izsak, P., & Wasserblat, M. (2019). Q8bert: Quantized 8bit bert. arXiv preprint arXiv:1910.06188.
[17]. Sanh, V., Wolf, T., & Rush, A. M. (2020). Movement pruning: Adaptive sparsity by fine-tuning. arXiv preprint arXiv:2005.07683.
[18]. Sanh, V., Debut, L., Chaumond, J., & Wolf, T. (2019). DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter. arXiv preprint arXiv:1910.01108.
[19]. Voita, E., Talbot, D., Moiseev, F., Sennrich, R., & Titov, I. (2019). Analyzing multi-head self-attention: Specialized heads do the heavy lifting, the rest can be pruned. arXiv preprint arXiv:1905.09418.
[20]. Voita, E., Talbot, D., Moiseev, F., Sennrich, R., & Titov, I. (2019). Analyzing multi-head self-attention: Specialized heads do the heavy lifting, the rest can be pruned. arXiv preprint arXiv:1905.09418.
Cite this article
Ren,M. (2024). Advancements and Applications of Large Language Models in Natural Language Processing: A Comprehensive Review. Applied and Computational Engineering,97,55-63.
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Volume title: Proceedings of the 2nd International Conference on Machine Learning and Automation
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