ChallengeMe: An Adversarial Learning-enabled Text Summarization Framework

2025-02-07

AI Summary

To address hallucination, overgeneralization, and insufficient domain specificity in large language models (LLMs) for vertical-domain text summarization, this paper proposes a three-stage adversarial learning–based prompting framework: generation–evaluation–feedback optimization. We innovatively introduce a cognition-inspired structured prompting mechanism, establishing seven quantifiable evaluation dimensions—accuracy, consistency, conciseness, among others—and design a dynamic adversarial threshold to jointly optimize summary quality and controllability. Experimental results demonstrate that our method significantly outperforms state-of-the-art LLMs on mixed-domain benchmarks: summary accuracy improves by 12.6% and linguistic fluency by 9.3% (p < 0.01). This work establishes a novel paradigm for controllable, domain-specific summarization.

Technology Category

• Natural Language Processing: Summarization
• Machine Learning: Large Multimodal Models (LMMs)
• Search and Optimization: Adversarial Search

Application Category

• Search and Retrieval-Augmented AI: Vertical and domain-specific search
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Economics, Online Markets and Human Computation: LLM based quality controls for crowd work

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.

Problem

• Addresses hallucination in LLMs
• Improves specificity in domain tasks
• Enhances text summarization accuracy

Innovation

• Adversarial learning-based prompt framework
• Includes generation, evaluation, feedback prompts
• Optimizes seven dimensions for accurate summaries