STAR is presented, a structure-aware adaptive retrieval framework for RAG that treats this mismatch as a problem of diagnosing evidence sufficiency and benefits from a control signal that preserves structurally distinct insufficiency patterns rather than collapsing them into a single scalar confidence estimate.
Retrieval evaluation for retrieval-augmented generation (RAG) is increasingly designed around whether retrieved passages contain evidence that can support generation, rather than topical relevance alone. We study whether this closer alignment with downstream evidence needs also makes retrieval evaluation more useful fo...
Utshab Kumar Ghosh, D. Mukhopadhyay, Shubham Chatterjee· 0 citations
A Bayesian evaluation framework is introduced that jointly models retrieval success, abstention behavior, and answer correctness, factorized according to the pipeline's information flow, and extends to incorporate LLM-as-a-judge annotations as calibrated noisy observations, enabling practitioners to combine limited hum...
Pius von Däniken, Felix Matthias Saaro, Mark Cieliebak et al.· 0 citations
Multimodal RAG retrieves text, tables, images, and videos, but choosing a retrieval granularity does not determine how much context to retain within each item. Coarse units include irrelevant content, while uniformly fine selection can remove context needed to interpret the evidence. Existing compressors address this t...
Hyojeong Yun, Jueun Kim, Wook-Shin Han· 0 citations
Large-taxonomy retrieval often assumes that the input already expresses the target concept. In many settings, however, the input is indirect evidence, such as a table cell whose meaning depends on its row, column, datatype, and context. We call this mismatch the retrieval readiness gap. Our analysis shows that the curr...
Lin-Hai Ma, Ethan F. Wei, Xue-Qing Peng et al.· 0 citations
Standard Retrieval-Augmented Generation (RAG) pipelines often provide no reliable inference-time signal of whether retrieval succeeded; on ambiguous or out-of-scope queries, generation may then hallucinate. Motivated by a Czech nuclear-regulator deployment where data sensitivity precludes third-party LLM APIs, we compa...
Adaptive RAG uses signals such as confidence, relevance, support, and retrieval quality to decide when to search or correct evidence. In multi-step retrieval, however, these local signals must be combined into a persistent view of what the current evidence supports, what remains missing, and which action should follow....
Hong-Ji Pu· 0 citations
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