Compact Prompting in Instruction-tuned LLMs for Joint Argumentative Component Detection

Researchers have proposed a novel methodology to enhance argumentative component detection (ACD), a demanding subtask within Argument Mining (AM) crucial for discourse analysis. ACD requires simultaneously identifying textual spans that constitute an argument and classifying them as specific components, such as claims or premises. Existing methods often simplify this joint delimitation and classification challenge, frequently reformulating it as a less complex sequence labeling problem. A recent paper on arXiv introduces an advanced technique utilizing instruction-tuned large language models (LLMs) through a novel approach called 'Compact Prompting'¹. This method aims to overcome the inherent complexities of accurately and jointly identifying these argumentative elements. By refining how prompts are constructed for LLMs, the research seeks to improve the precision and integration of segmenting and categorizing arguments across diverse text types. This progression in automated argument analysis provides sophisticated capabilities for dissecting intricate information, offering vital insights for fields ranging from policy formulation and security intelligence to comprehending societal impacts on workforce trends.