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[QA] Found in the Middle: Calibrating Positional Attention Bias Improves Long Context Utilization
Manage episode 426208885 series 3524393
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
--- Support this podcast: https://podcasters.spotify.com/pod/show/arxiv-papers/support
1533 episodes
Manage episode 426208885 series 3524393
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
--- Support this podcast: https://podcasters.spotify.com/pod/show/arxiv-papers/support
1533 episodes
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