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cs.CL2026
How Fine-Grained Should a RAG Benchmark Be? A Hierarchical Framework for Synthetic Question Generation
Chase M. Fensore, Kaustubh Dhole, Jason Fan +2
Evaluating retrieval-augmented generation (RAG) systems requires benchmarks that capture diverse question characteristics, yet practitioners lack empirical guidance on which dimens…
cs.CL2025
MoC: Mixtures of Text Chunking Learners for Retrieval-Augmented Generation System
Jihao Zhao, Zhiyuan Ji, Zhaoxin Fan +5
Retrieval-Augmented Generation (RAG), while serving as a viable complement to large language models (LLMs), often overlooks the crucial aspect of text chunking within its pipeline.…