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

Researchers developed a novel approach using instruction-tuned Large Language Models to improve argumentative component detection in text analysis. The method reframes the task as language generation rather than traditional sequence labeling, achieving superior performance on standard benchmarks compared to existing state-of-the-art systems.

• →New LLM-based approach outperforms existing methods for detecting argumentative components like claims and premises in text.
• →Researchers reframed argumentative component detection as a generative language task rather than sequence labeling.
• →The method uses compact instruction-based prompts to identify arguments directly from plain text.
• →This represents one of the first attempts to fully model argumentative component detection as a generative task.
• →Results demonstrate the potential of instruction tuning for complex argument mining problems.