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...
I tiakina i:
| Ngā kaituhi matua: | , |
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| Hōputu: | Artigo |
| Reo: | Inglês |
| I whakaputaina: |
Springer
2026-05-01
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| Rangatū: | Discover Computing |
| Ngā marau: | |
| Urunga tuihono: | https://doi.org/10.1007/s10791-026-10156-3 |
| Ngā Tūtohu: |
Kāore He Tūtohu, Me noho koe te mea tuatahi ki te tūtohu i tēnei pūkete!
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