English(EN) FATHOMS-RAG: A Framework for the Assessment of Thinking and Observation in Multimodal Systems that use Retrieval Augmented Generation

新的基准评估多模态RAG系统

作者 PulseAugur 编辑部 · [1 个来源] · 2026-05-25 04:00

研究人员开发了FATHOMS-RAG，这是一个旨在评估检索增强生成（RAG）系统端到端性能的新基准。该框架评估RAG管道在文本、表格和图像等各种数据模态中摄取、检索和推理的能力。研究发现，闭源RAG管道的性能通常优于开源管道，尤其是在处理复杂的多模态和跨文档信息时。 AI

影响为多模态RAG系统引入了新的评估框架，有望提高其准确性并减少幻觉。

排序理由该集群包含一篇介绍AI系统评估新基准的研究论文。[lever_c_demoted from research: ic=1 ai=1.0]

AI 生成摘要 · Google Gemini · 来自 1 个来源。我们如何撰写摘要 →

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arXiv cs.AI TIER_1 English(EN) · Samuel Hildebrand (Louisiana State University), Curtis Taylor (Oak Ridge National Lab), Sean Oesch (Oak Ridge National Lab), James M Ghawaly Jr (Louisiana State University), Amir Sadovnik (Oak Ridge National Lab), Ryan Shivers (Oak Ridge National Lab), B… · 2026-05-25 04:00

FATHOMS-RAG: A Framework for the Assessment of Thinking and Observation in Multimodal Systems that use Retrieval Augmented Generation

arXiv:2510.08945v3 Announce Type: replace Abstract: Retrieval-augmented generation (RAG) has emerged as a promising paradigm for improving factual accuracy in large language models (LLMs). We introduce a benchmark designed to evaluate RAG pipelines as a whole, evaluating a pipeli…