通过推理链提升AI生成内容检测的准确性和可解释性。
Reasoning-Aware AIGC Detection via Alignment and Reinforcement
- 先训练推理能力，再用强化学习优化逻辑一致性
- 在多领域数据集上达到当前最佳检测效果
- 适合需要透明、可信检测结果的研究与应用
大型语言模型的快速发展加剧了对可靠AI生成内容（AIGC）检测的需求，但随着模型演进，检测难度持续上升。本文构建了AIGC-text-bank，一个涵盖多领域、多种LLM来源与作者场景的综合性数据集，并提出REVEAL检测框架，该框架在分类前生成可解释的推理链。方法采用两阶段训练：先通过监督微调建立推理能力，再通过强化学习提升准确性、逻辑一致性和降低幻觉。大量实验表明，REVEAL在多个基准测试中表现优于现有方法，提供了鲁棒且透明的AIGC检测方案。项目已开源，地址为 https://aka.ms/reveal。
原文摘要 · Abstract (English)
The rapid advancement and widespread adoption of Large Language Models (LLMs) have elevated the need for reliable AI-generated content (AIGC) detection, which remains challenging as models evolve. We introduce AIGC-text-bank, a comprehensive multi-domain dataset with diverse LLM sources and authorship scenarios, and propose REVEAL, a detection framework that generates interpretable reasoning chains before classification. Our approach uses a two-stage training strategy: supervised fine-tuning to establish reasoning capabilities, followed by reinforcement learning to improve accuracy, improve logical consistency, and reduce hallucinations. Extensive experiments show that REVEAL achieves state-of-the-art performance across multiple benchmarks, offering a robust and transparent solution for AIGC detection. The project is open-source at https://aka.ms/reveal
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。