Assessing Open-Weight Large Language Models on Argumentation Mining Subtasks

Mohammad Yeghaneh Abkenar, Weixing Wang, Hendrik Graupner, Manfred Stede

Abstract: Weexplore the capability of four open-weight large 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 subtasks: (i) argument component type classif ication (ACTC), and (ii) argumentative relation classification (ARC). This work aims to assess the argumentation capability of openweight LLMs, including Mistral 7B, Mixtral 8x7B, LLaMA2 7B and LLaMA38Binboth, zero-shot and few-shot scenarios. Our results demonstrate that open-weight LLMs can effectively tackle argumentation mining subtasks, with context-aware prompting improving relation classification performance, though the models’ effectiveness varies across different argumentation patterns and corpus types, suggesting potential for specialized adaptation in future argumentation systems. Our analysis advances the assessment of computational argumentation capabilities in open-weight LLMs and provides a foundation for future research