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[QA] Found in the Middle: Calibrating Positional Attention Bias Improves Long Context Utilization
Large language models struggle with capturing relevant information in the middle of input due to intrinsic attention bias. Mitigating bias with found-in-the-middle mechanism improves performance and RAG outcomes significantly.
https://arxiv.org/abs//2406.16008
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
Large language models struggle with capturing relevant information in the middle of input due to intrinsic attention bias. Mitigating bias with found-in-the-middle mechanism improves performance and RAG outcomes significantly.
https://arxiv.org/abs//2406.16008
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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