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[QA] In Defense of RAG in the Era of Long-Context Language Models
This paper introduces OP-RAG, enhancing retrieval-augmented generation for long-context applications, demonstrating improved answer quality compared to long-context LLMs through optimized retrieval strategies.
https://arxiv.org/abs//2409.01666
YouTube: https://www.youtube.com/@ArxivPapers
TikTok: https://www.tiktok.com/@arxiv_papers
Apple Podcasts: https://podcasts.apple.com/us/podcast/arxiv-papers/id1692476016
Spotify: https://podcasters.spotify.com/pod/show/arxiv-papers
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By Igor Melnyk
5
33 ratings
This paper introduces OP-RAG, enhancing retrieval-augmented generation for long-context applications, demonstrating improved answer quality compared to long-context LLMs through optimized retrieval strategies.
https://arxiv.org/abs//2409.01666
YouTube: https://www.youtube.com/@ArxivPapers
TikTok: https://www.tiktok.com/@arxiv_papers
Apple Podcasts: https://podcasts.apple.com/us/podcast/arxiv-papers/id1692476016
Spotify: https://podcasters.spotify.com/pod/show/arxiv-papers
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