用对抗学习提升大模型摘要准确性，减少幻觉。
ChallengeMe: An Adversarial Learning-enabled Text Summarization Framework
- 设计三阶段对抗框架：生成、评估、反馈优化
- 在摘要任务中优于主流大模型，准确性和流畅性提升
- 适合需要高可靠性的垂直领域文本生成场景
大型语言模型（LLMs）在生产与日常应用中表现出色，但在垂直领域任务中仍面临幻觉和生成内容缺乏具体性的问题。受人类认知过程中的对比与分类机制启发，本文构建了一个基于对抗学习的提示框架ChallengeMe，包含生成提示、评估提示和反馈优化三个级联模块。设计了七个核心优化维度，并设定了对抗学习的阈值。在文本摘要任务的混合案例研究中，该框架生成的摘要比当前主流先进大模型更准确、更流畅。
原文摘要 · Abstract (English)
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.
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