为了更好地评估检索增强生成 (RAG) 系统在跨领域问答中的能力，研究人员开发了长篇RobustQA (LFRQA) 数据集。LFRQA 包含来自多个文档的整合信息，提供长篇连贯的答案，涵盖七个领域，共计 26,000 个查询。该数据集旨在评估基于大型语言模型 (LLM) 的 RAG-QA 系统在跨领域泛化能力。LFRQA 还引入了 RAG-QA Arena 框架，使用模型评估器将 LLM 生成的答案与 LFRQA 的人工答案进行比较，提供更准确和具有挑战性的基准。

🤔 LFRQA 数据集包含来自多个文档的整合信息，提供长篇连贯的答案，涵盖七个领域，共计 26,000 个查询。旨在评估基于大型语言模型 (LLM) 的 RAG-QA 系统在跨领域泛化能力。

💪 RAG-QA Arena 框架使用模型评估器将 LLM 生成的答案与 LFRQA 的人工答案进行比较，提供更准确和具有挑战性的基准。

📊 实验结果表明，LFRQA 答案在 59.1% 的情况下优于领先的 LLM 答案，表明 LFRQA 在评估 RAG-QA 系统的跨领域鲁棒性方面具有重要价值。

🚀 LFRQA 包含详细的性能指标，提供关于 QA 系统有效性的宝贵见解，例如用于生成答案的文档数量、答案的一致性和流畅性。

💡 研究人员通过整合来自多个文档的信息，并进行严格的质量控制，确保了 LFRQA 数据集的高质量。

📚 LFRQA 数据集和 RAG-QA Arena 框架为评估 RAG-QA 系统的跨领域鲁棒性提供了更全面的基准，为 NLP 和 QA 研究的进步做出了重要贡献。

Question answering (QA) is a crucial area in natural language processing (NLP), focusing on developing systems that can accurately retrieve and generate responses to user queries from extensive data sources. Retrieval-augmented generation (RAG)  enhances the quality and relevance of answers by combining information retrieval with text generation. This approach filters out irrelevant information and presents only the most pertinent passages for large language models (LLMs) to generate responses.

One of the main challenges in QA is the limited scope of existing datasets, which often use single-source corpora or focus on short, extractive answers. This limitation hampers evaluating how well LLMs can generalize across different domains. Current methods such as Natural Questions and TriviaQA rely heavily on Wikipedia or web documents, which are insufficient for assessing cross-domain performance. As a result, there is a significant need for more comprehensive evaluation frameworks that can test the robustness of QA systems across various domains.

Researchers from AWS AI Labs, Google, Samaya.ai, Orby.ai, and the University of California, Santa Barbara, have introduced Long-form RobustQA (LFRQA) to address these limitations. This new dataset comprises human-written long-form answers that integrate information from multiple documents into coherent narratives. Covering 26,000 queries across seven domains, LFRQA aims to evaluate the cross-domain generalization capabilities of LLM-based RAG-QA systems.

LFRQA distinguishes itself from previous datasets by offering long-form answers grounded in a corpus, ensuring coherence, and covering multiple domains. The dataset includes annotations from various sources, making it a valuable tool for benchmarking QA systems. This approach addresses the shortcomings of extractive QA datasets, which often fail to capture the comprehensive and detailed nature of modern LLM responses.

The research team introduced the RAG-QA Arena framework to leverage LFRQA for evaluating QA systems. This framework employs model-based evaluators to directly compare LLM-generated answers with LFRQA’s human-written answers. By focusing on long-form, coherent answers, RAG-QA Arena provides a more accurate and challenging benchmark for QA systems. Extensive experiments demonstrated a high correlation between model-based and human evaluations, validating the framework’s effectiveness.

The researchers employed various methods to ensure the high quality of LFRQA. Annotators were instructed to combine short extractive answers into coherent long-form answers, incorporating additional information from the documents when necessary. Quality control measures included random audits of annotations to ensure completeness, coherence, and relevance. This rigorous process resulted in a dataset that effectively benchmarks the cross-domain robustness of QA systems.

Performance results from the RAG-QA Arena framework show significant findings. Only 41.3% of answers generated by the most competitive LLMs were preferred over LFRQA’s human-written answers. The dataset demonstrated a strong correlation between model-based and human evaluations, with a correlation coefficient of 0.82. Furthermore, the evaluation revealed that LFRQA answers, which integrated information from up to 80 documents, were preferred in 59.1% of cases compared to leading LLM answers. The framework also highlighted a 25.1% gap in performance between in-domain and out-of-domain data, emphasizing the importance of cross-domain evaluation in developing robust QA systems.

In addition to its comprehensive nature, LFRQA includes detailed performance metrics that provide valuable insights into the effectiveness of QA systems. For example, the dataset contains information about the number of documents used to generate answers, the coherence of those answers, and their fluency. These metrics help researchers understand the strengths and weaknesses of different QA approaches, guiding future improvements.

In conclusion, the research led by AWS AI Labs, Google, Samaya.ai, Orby.ai, and the University of California, Santa Barbara, highlights the limitations of existing QA evaluation methods and introduces LFRQA and RAG-QA Arena as innovative solutions. These tools offer a more comprehensive and challenging benchmark for assessing the cross-domain robustness of QA systems, contributing significantly to the advancement of NLP and QA research.

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