EnronQA：面向私有文档的个性化RAG
EnronQA: Towards Personalized RAG over Private Documents
AI总结：
针对现有RAG基准缺乏私有数据与个人上下文的问题，研究发布了包含103,638封邮件和528,304个问答对的EnronQA基准，以支持私有文档的个性化RAG评估，并探索记忆与检索的权衡。
摘要：
检索增强生成（RAG）已成为为大型语言模型（LLM）引入知识密集型上下文的最流行方法之一，因为它能够在推理时引入局部上下文，且没有微调相关的成本或数据泄露风险。将私有信息与LLM训练进行明确分离，使得RAG成为许多企业LLM工作负载的基础，因为它允许公司使用客户的私有文档来增强LLM的理解能力。尽管RAG在企业部署的私有文档处理中广受欢迎，但当前用于验证和优化RAG流程的基准测试主要从维基百科或通用网页等公开数据中提取语料库，几乎不提供任何个人上下文。为了推动更具个性化和私有化的RAG，我们发布了EnronQA基准，这是一个包含103,638封电子邮件和跨越150个不同用户收件箱的528,304个问答对的数据集。EnronQA能够更好地对私有数据上的RAG流程进行基准测试，并允许在真实数据上对引入个性化检索设置进行实验。最后，我们使用EnronQA来探索在私有文档上进行推理时，记忆与检索之间的权衡。
英文摘要：
Retrieval Augmented Generation (RAG) has become one of the most popular methods for bringing knowledge-intensive context to large language models (LLM) because of its ability to bring local context at inference time without the cost or data leakage risks associated with fine-tuning. A clear separation of private information from the LLM training has made RAG the basis for many enterprise LLM workloads as it allows the company to augment LLM's understanding using customers' private documents. Despite its popularity for private documents in enterprise deployments, current RAG benchmarks for validating and optimizing RAG pipelines draw their corpora from public data such as Wikipedia or generic web pages and offer little to no personal context. Seeking to empower more personal and private RAG we release the EnronQA benchmark, a dataset of 103,638 emails with 528,304 question-answer pairs across 150 different user inboxes. EnronQA enables better benchmarking of RAG pipelines over private data and allows for experimentation on the introduction of personalized retrieval settings over realistic data. Finally, we use EnronQA to explore the tradeoff in memorization and retrieval when reasoning over private documents.
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