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Assessing Open-Source Large Language Models on Argumentation Mining Subtasks
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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.
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AMELIA: A Family of Multi-task End-to-end Language Models for Argumentation
A single LoRA fine-tuned Llama-3.1-8B-Instruct model trained jointly on eight argument-mining tasks across 19 datasets matches or beats task-specific models, and merged models offer a cheaper compromise.
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