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

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

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

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

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

Popis výsledku anglicky

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.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

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

Projekt

—

Návaznosti

—

Rok uplatnění

2025

Kód důvěrnosti údajů

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

Název statě ve sborníku

Commun. Comput. Info. Sci.

ISBN

978-3-031-96472-5

ISSN

18650929

e-ISSN

—

Počet stran výsledku

18

Strana od-do

317-334

Název nakladatele

Springer Science and Business Media Deutschland GmbH

Místo vydání

—

Místo konání akce

Bangalore

Datum konání akce

1. 1. 2026

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

—