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

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F26%3A8MUEUWF4" target="_blank" >RIV/00216208:11320/26:8MUEUWF4 - isvavai.cz</a>

Result on the web

<a href="http://dx.doi.org/10.1007/978-3-031-96473-2_22" target="_blank" >http://dx.doi.org/10.1007/978-3-031-96473-2_22</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-031-96473-2_22" target="_blank" >10.1007/978-3-031-96473-2_22</a>

Result language

angličtina

Original language name

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

Original language description

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. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

Czech name

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Czech description

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Type

D - Article in proceedings

CEP classification

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OECD FORD branch

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project

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Continuities

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Publication year

2025

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection

Commun. Comput. Info. Sci.

ISBN

978-3-031-96472-5

ISSN

18650929

e-ISSN

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Number of pages

18

Pages from-to

317-334

Publisher name

Springer Science and Business Media Deutschland GmbH

Place of publication

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Event location

Bangalore

Event date

Jan 1, 2026

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

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