ChallengeMe: An Adversarial Learning-enabled Text Summarization Framework

TL;DR

ChallengeMe is an adversarial learning framework that improves text summarization by addressing hallucination and specificity issues in large language models, using cascaded prompts and feedback optimization.

Contribution

It introduces a novel adversarial prompt framework with three cascaded solutions and seven optimization dimensions to enhance summarization accuracy and fluency.

Findings

Outperforms current mainstream LLMs in accuracy and fluency

Effectively reduces hallucination in generated summaries

Demonstrates robustness across multiple case studies

Abstract

The astonishing performance of large language models (LLMs) and their remarkable achievements in production and daily life have led to their widespread application in collaborative tasks. However, current large models face challenges such as hallucination and lack of specificity in content generation in vertical domain tasks. Inspired by the contrast and classification mechanisms in human cognitive processes, this paper constructs an adversarial learning-based prompt framework named ChallengeMe, which includes three cascaded solutions: generation prompts, evaluation prompts, and feedback optimization. In this process, we designed seven core optimization dimensions and set the threshold for adversarial learning. The results of mixed case studies on the text summarization task show that the proposed framework can generate more accurate and fluent text summaries compared to the current advanced mainstream LLMs.