轻量模型实时检测大模型生成文本是否脱离上下文，保障企业RAG应用安全。
RAGulator: Lightweight Out-of-Context Detectors for Grounded Text Generation
- 用摘要与语义相似度数据构建训练集，仅需少量资源。
- DeBERTa模型在检测任务中表现最佳且推理速度快。
- 适合对响应速度和本地部署有要求的工业级应用场景。
实时检测大模型生成内容是否脱离上下文，对希望安全采用RAG应用的企业至关重要。本文训练轻量级模型，区分大模型生成文本与检索文档之间的语义偏离情况。通过结合摘要与语义文本相似性数据集，以最少资源预处理构建训练数据。实验发现，DeBERTa不仅在此流程下表现最优，且推理速度快，无需额外文本预处理或特征工程。尽管已有研究证明生成式大模型可通过微调融入复杂数据管道实现顶尖性能，但考虑到本地部署中的速度与资源限制，本方法更具实用性。
原文摘要 · Abstract (English)
Real-time detection of out-of-context LLM outputs is crucial for enterprises looking to safely adopt RAG applications. In this work, we train lightweight models to discriminate LLM-generated text that is semantically out-of-context from retrieved text documents. We preprocess a combination of summarisation and semantic textual similarity datasets to construct training data using minimal resources. We find that DeBERTa is not only the best-performing model under this pipeline, but it is also fast and does not require additional text preprocessing or feature engineering. While emerging work demonstrates that generative LLMs can also be fine-tuned and used in complex data pipelines to achieve state-of-the-art performance, we note that speed and resource limits are important considerations for on-premise deployment.
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