Chunk-as-a-Service with the UCOSA online algorithm enables budget-constrained selection of prompts for chunk enrichment in RAG, outperforming random selection by 52% on a combined performance metric and delivering higher performance-to-budget ratios than standard RaaS.
Retrieval- augmented generation for knowledge-intensive nlp tasks
2 Pith papers cite this work. Polarity classification is still indexing.
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UNVERDICTED 2representative citing papers
AdaRankLLM shows adaptive listwise reranking outperforms fixed-depth retrieval for most LLMs by acting as a noise filter for weak models and an efficiency optimizer for strong ones, with lower context use.
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Budget-Constrained Online Retrieval-Augmented Generation: The Chunk-as-a-Service Model
Chunk-as-a-Service with the UCOSA online algorithm enables budget-constrained selection of prompts for chunk enrichment in RAG, outperforming random selection by 52% on a combined performance metric and delivering higher performance-to-budget ratios than standard RaaS.
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Rethinking the Necessity of Adaptive Retrieval-Augmented Generation through the Lens of Adaptive Listwise Ranking
AdaRankLLM shows adaptive listwise reranking outperforms fixed-depth retrieval for most LLMs by acting as a noise filter for weak models and an efficiency optimizer for strong ones, with lower context use.