Assessing Open-Source Large Language Models on Argumentation Mining Subtasks

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Main Authors: Abkenar, Mohammad Yeghaneh, Wang, Weixing, Graupner, Hendrik, Stede, Manfred
Format: Preprint
Published: 2024
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author Abkenar, Mohammad Yeghaneh
Wang, Weixing
Graupner, Hendrik
Stede, Manfred
author_facet Abkenar, Mohammad Yeghaneh
Wang, Weixing
Graupner, Hendrik
Stede, Manfred
contents We explore the capability of four open-sourcelarge language models (LLMs) in argumentation mining (AM). We conduct experiments on three different corpora; persuasive essays(PE), argumentative microtexts (AMT) Part 1 and Part 2, based on two argumentation mining sub-tasks: (i) argumentative discourse units classifications (ADUC), and (ii) argumentative relation classification (ARC). This work aims to assess the argumentation capability of open-source LLMs, including Mistral 7B, Mixtral8x7B, LlamA2 7B and LlamA3 8B in both, zero-shot and few-shot scenarios. Our analysis contributes to further assessing computational argumentation with open-source LLMs in future research efforts.
format Preprint
id arxiv_https___arxiv_org_abs_2411_05639
institution arXiv
publishDate 2024
record_format arxiv
spellingShingle Assessing Open-Source Large Language Models on Argumentation Mining Subtasks
Abkenar, Mohammad Yeghaneh
Wang, Weixing
Graupner, Hendrik
Stede, Manfred
Computation and Language
We explore the capability of four open-sourcelarge language models (LLMs) in argumentation mining (AM). We conduct experiments on three different corpora; persuasive essays(PE), argumentative microtexts (AMT) Part 1 and Part 2, based on two argumentation mining sub-tasks: (i) argumentative discourse units classifications (ADUC), and (ii) argumentative relation classification (ARC). This work aims to assess the argumentation capability of open-source LLMs, including Mistral 7B, Mixtral8x7B, LlamA2 7B and LlamA3 8B in both, zero-shot and few-shot scenarios. Our analysis contributes to further assessing computational argumentation with open-source LLMs in future research efforts.
title Assessing Open-Source Large Language Models on Argumentation Mining Subtasks
topic Computation and Language
url https://arxiv.org/abs/2411.05639