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This paper comprehensively reviews the application of large language models (LLMs) for dependency parsing, focusing on auto-regressive models such as LLaMA (Large Language Model Meta AI). Dependency parsing is a crucial task in natural language processing (NLP), essential for understanding the syntactic structure of sentences. This review traces the evolution of dependency parsing techniques and highlights the significant advancements brought by LLMs. We provide an in-depth analysis of LLaMA’s performance, efficiency, and multilingual capabilities, comparing it with other state-of-the-art models like GPT-3 and BERT. Our findings reveal that LLaMA achieves state-of-the-art results with fewer computational resources, excels in multilingual contexts, and demonstrates robustness in parsing raw sentences. We also discuss the ethical considerations, challenges, and future directions in the field, providing valuable insights for researchers and practitioners.

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Comprehensive Review of End-to-End Dependency Parsing with Auto-regressive Large Language Models

  • 会议论文

  • 发表日期：

    2025年

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  • Nagarchi Arshad Ali,
  • K. Ramalakshmi

摘要

This paper comprehensively reviews the application of large language models (LLMs) for dependency parsing, focusing on auto-regressive models such as LLaMA (Large Language Model Meta AI). Dependency parsing is a crucial task in natural language processing (NLP), essential for understanding the syntactic structure of sentences. This review traces the evolution of dependency parsing techniques and highlights the significant advancements brought by LLMs. We provide an in-depth analysis of LLaMA’s performance, efficiency, and multilingual capabilities, comparing it with other state-of-the-art models like GPT-3 and BERT. Our findings reveal that LLaMA achieves state-of-the-art results with fewer computational resources, excels in multilingual contexts, and demonstrates robustness in parsing raw sentences. We also discuss the ethical considerations, challenges, and future directions in the field, providing valuable insights for researchers and practitioners.