Towards Robust Argumentative Essay Understanding via TIDE: An Interactive Framework with Trial and Debate

TL;DR

This paper introduces TIDE, an interactive framework that enhances argument essay understanding by combining trial and debate mechanisms to improve prompt optimization and task performance.

Contribution

The paper presents a novel TIDE framework that addresses noise and stability issues in prompt-based argument understanding tasks.

Findings

TIDE improves performance on Automated Essay Scoring.

TIDE enhances Argument Component Detection accuracy.

TIDE advances Argument Relation Identification results.

Abstract

Argumentative essays serve as a vital medium for assessing critical thinking and reasoning skills, yet there is limited works on accurately understanding and evaluating such texts via prompt. In this work, we propose TIDE, a novel framework designed to improve criteria-based prompt optimization for argument-related tasks by integrating TrIal and DEbate mechanism. Our method addresses key limitations of criteria-based prompt optimizing by mitigating the influence of noisy training data and enhancing optimization stability. We evaluate TIDE on three core tasks: Automated Essay Scoring, Argument Component Detection, and Argument Relation Identification. Results demonstrate that our framework improves performance across tasks. These findings underscore the potential of combining prompt-based methods for advanced argument understanding.