Evaluating retriever reranker pairings in RAG based on quality and efficiency trade-offs
Abstract Large language models (LLMs) are the core of many Artificial Intelligence (AI) systems. One of the key problems with these systems is hallucination (i.e., making up facts). Retrieval-Augmented Generation (RAG) solves this problem by grounding responses in external knowledge sources, thereby...
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| Principais autores: | , |
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| Formato: | Artigo |
| Idioma: | Inglês |
| Publicado: |
Springer
2026-05-01
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| Series: | Discover Computing |
| Assuntos: | |
| Acceso en liña: | https://doi.org/10.1007/s10791-026-10156-3 |
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