忠实性问答：一个用于训练上下文忠实RAG模型的反事实实体替换数据集
Faithfulness-QA: A Counterfactual Entity Substitution Dataset for Training Context-Faithful RAG Models
AI总结：
本文提出Faithfulness-QA数据集，通过反事实实体替换生成可控的知识冲突，旨在训练上下文忠实的RAG模型并评估其上下文接地行为。
摘要：
检索增强生成（RAG）模型经常产生基于参数记忆而非检索上下文的答案，削弱了检索增强的核心承诺。解决这种不忠实性的根本障碍是缺乏要求模型优先考虑上下文而非内部知识的训练数据。我们引入了Faithfulness-QA，一个包含99,094个样本的大规模数据集，通过反事实实体替换构建。从两个已建立的提取性问答基准-SQuAD和TriviaQA出发，我们自动识别每个上下文中的答案承载命名实体，用来自精心编纂的76,953个实体库中的一致替代品替换它们，从而在上下文和参数记忆之间制造可控的知识冲突。严格的质量过滤确保在随机200样本审核中四个自动化检查的100%通过率。我们发布了完整数据集、构建流程和涵盖八个命名实体类别的类型化实体库。Faithfulness-QA被设计为用于注意机制忠实性目标的训练资源，以及用于测量RAG系统上下文接地行为的评估基准。数据和代码可在https://github.com/qzhangFDU/faithfulness-qa-dataset获取。
英文摘要：
Retrieval-Augmented Generation (RAG) models frequently produce answers grounded in parametric memory rather than the retrieved context, undermining the core promise of retrieval augmentation. A fundamental obstacle to fixing this unfaithfulness is the lack of training data that explicitly requires models to prefer context over internal knowledge. We introduce Faithfulness-QA, a large-scale dataset of 99,094 samples constructed through counterfactual entity substitution. Starting from two established extractive QA benchmarks--SQuAD and TriviaQA--we automatically identify answer-bearing named entities in each context, replace them with type-consistent alternatives drawn from a curated bank of 76,953 entities, and thereby manufacture controlled knowledge conflicts between context and parametric memory. Rigorous quality filtering ensures 100% pass rates across four automated checks on random 200-sample audits. We release the full dataset, the construction pipeline, and a typed entity bank covering eight named entity categories. Faithfulness-QA is designed as a training resource for attention-based faithfulness objectives and as an evaluation benchmark for measuring context-grounding behavior in RAG systems. Data and code are available at https://github.com/qzhangFDU/faithfulness-qa-dataset.
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。
