Extracting Arguments, Not Just Classifying Them: Instruction-Tuned LLMs for Generative Component Detection

Problem

Argumentative component detection (ACD) is a critical subtask within Argument Mining (AM) that involves identifying and classifying spans of text that contain argumentative components. This paper addresses the limitations of existing ACD methods by proposing a novel approach that leverages instruction-tuned Large Language Models (LLMs). The work is presented as a preprint and has not yet undergone peer review.

Method

The authors introduce ITFACD, which reformulates the ACD task as a language generation problem. This approach allows the model to generate the necessary outputs directly from plain text inputs without requiring pre-segmented components. The model utilizes compact instruction-based prompts to guide the generation process, enhancing its ability to detect and classify argumentative components effectively. The specific architecture of the LLM used in ITFACD is not disclosed, nor are the details regarding the training compute or dataset specifics.

Results

The available text does not report quantitative results. However, the authors claim that ITFACD achieves higher performance compared to state-of-the-art systems in the field, although no specific baselines or benchmarks are provided for comparison.

Limitations

The authors do not report any limitations in their work. However, the lack of quantitative results and specific comparisons to existing methods may hinder the assessment of the model's effectiveness relative to other approaches in ACD.

Why it matters

The implications of this work are significant for downstream applications in argument mining and related fields. By framing ACD as a generative task, ITFACD may enable more nuanced understanding and extraction of argumentative structures in text, potentially improving the performance of systems that rely on argumentation analysis. This approach could pave the way for further advancements in the integration of LLMs in complex text analysis tasks.