Argument Mining with Fine-Tuned Large Language Models
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985807%3A_____%2F25%3A00602750" target="_blank" >RIV/67985807:_____/25:00602750 - isvavai.cz</a>
Result on the web
<a href="https://aclanthology.org/2025.coling-main.442/" target="_blank" >https://aclanthology.org/2025.coling-main.442/</a>
DOI - Digital Object Identifier
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Alternative languages
Result language
angličtina
Original language name
Argument Mining with Fine-Tuned Large Language Models
Original language description
An end-to-end argument mining (AM) pipeline takes a text as input and provides its argumentative structure as output by identifying and classifying the argument units and argument relations in the text. In this work, we approach AM using fine-tuned large language models (LLMs). We model the three main sub-tasks of the AM pipeline, as well as their joint formulation, as text generation tasks. We finetune eight popular quantized and non-quantized LLMs – LLaMA-3, LLaMA-3.1, Gemma-2, Mistral, Phi-3, Qwen-2 – which are among the most capable open-weight models, on the benchmark PE, AbstRCT, and CDCP datasets that represent diverse data sources. Our approach achieves state-of-the-art results across all AM sub-tasks and datasets, showing significant improvements over previous benchmarks.
Czech name
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Czech description
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Classification
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)
Result continuities
Project
<a href="/en/project/GA22-02067S" target="_blank" >GA22-02067S: AppNeCo: Approximate Neurocomputing</a><br>
Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2025
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
COLING 2025: The 31st International Conference on Computational Linguistics. Proceedings of the Main Conference
ISBN
979-8-89176-196-4
ISSN
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e-ISSN
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Number of pages
12
Pages from-to
6624-6635
Publisher name
The Association for Computational Linguistics
Place of publication
Stroudsburg
Event location
Abu Dhabi
Event date
Jan 19, 2025
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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