透明自然语言处理：利用RAG和LLM对齐技术实现隐私问答
Transparent NLP: Using RAG and LLM Alignment for Privacy Q&A
AI总结：
针对GDPR透明度原则下语言模型的局限性，研究结合对齐技术的RAG系统（含RAIN及扩展MultiRAIN），通过隐私问答数据集及21个指标评估，发现其优于基线但未达人类水平，为合规框架集成先进NLP提供基础。
摘要：
《通用数据保护条例》（GDPR）的透明度原则要求数据处理信息清晰、准确且可访问。尽管语言模型在该场景下展现出潜力，但其概率性本质导致真实性和可理解性复杂化。本文研究了结合对齐技术的最先进检索增强生成（RAG）系统，以满足GDPR义务。我们使用隐私问答数据集评估了集成对齐模块（如可回卷自回归推理（RAIN）和我们提出的多维扩展MultiRAIN）的RAG系统。响应针对准确性和可理解性进行优化，并通过21个指标评估，包括确定性评估和基于大型语言模型的评估。结果显示，带有对齐模块的RAG系统在大多数指标上优于基线RAG系统，但均未完全达到人类答案水平。结果的主成分分析揭示了指标间的复杂相互作用，强调了优化指标的必要性。本研究为将先进自然语言处理系统整合到合规框架提供了基础。
英文摘要：
The transparency principle of the General Data Protection Regulation (GDPR) requires data processing information to be clear, precise, and accessible. While language models show promise in this context, their probabilistic nature complicates truthfulness and comprehensibility. This paper examines state-of-the-art Retrieval Augmented Generation (RAG) systems enhanced with alignment techniques to fulfill GDPR obligations. We evaluate RAG systems incorporating an alignment module like Rewindable Auto-regressive Inference (RAIN) and our proposed multidimensional extension, MultiRAIN, using a Privacy Q&A dataset. Responses are optimized for preciseness and comprehensibility and are assessed through 21 metrics, including deterministic and large language model-based evaluations. Our results show that RAG systems with an alignment module outperform baseline RAG systems on most metrics, though none fully match human answers. Principal component analysis of the results reveals complex interactions between metrics, highlighting the need to refine metrics. This study provides a foundation for integrating advanced natural language processing systems into legal compliance frameworks.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
