Summary of Can Large Language Models Perform Relation-based Argument Mining?, by Deniz Gorur et al.
Can Large Language Models perform Relation-based Argument Mining?
by Deniz Gorur, Antonio Rago, Francesca Toni
First submitted to arxiv on: 17 Feb 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper proposes a novel approach to argument mining, a crucial task for analyzing online debates on various platforms. The researchers focus on relation-based argument mining, which identifies support and attack relations between arguments. Current methods have shown limited success in this task, prompting the investigation of general-purpose Large Language Models (LLMs) as potential solutions. The study finds that LLMs, when primed and prompted correctly, can outperform state-of-the-art baselines, including RoBERTa-based models. Specifically, two open-source LLMs, Llama-2 and Mistral, are tested on ten datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Online debates are happening more often on various platforms, so we need to find better ways to understand what’s being said. One way to do this is by analyzing the arguments people use. This paper shows that a special type of computer model called a Large Language Model can help us do just that. These models can analyze text and find patterns that humans might miss. In this case, they’re used to identify relationships between different ideas or opinions expressed in a piece of writing. The researchers tested these models on ten different datasets and found that they performed much better than previous methods. |
Keywords
» Artificial intelligence » Large language model » Llama » Prompting
