ChallengeMe: An Adversarial Learning-enabled Text Summarization Framework

TL;DR

ChallengeMe tackles hallucination and content specificity in LLM-based summarization by emulating human contrastive learning through a dual-branch generator-inspector and a threshold-based feedback loop. The method constructs targeted generation prompts and a multi-dimensional detector, with $S_{out} = g(f(T_{in}), Prompt)$ and a gating rule that requires $F_{fluency}, F_{consistency}, F_{naturalness} \\ge T_{min}$ where $T_{min}=7$. A three-part framework (prompt generation, prompt detection, and feedback optimization) supports iterative rounds to improve fluency, coherence, and factuality. Empirical results on CNN/Daily Mail, BillSum, and arXiv summarization show state-of-the-art metrics (Rouge, Bleu, Bertscore) and favorable human evaluations, demonstrating practical impact for domain-specific AI assistants in summarization tasks.

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.

Figures (1)

• Figure 1: Fig. Framework of the proposed ChallengeMe