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Kernel Language Entropy: Fine-grained Uncertainty Quantification for LLMs from Semantic Similarities
Kernel Language Entropy (KLE) method improves uncertainty quantification in Large Language Models (LLMs) by capturing semantic uncertainty, enhancing trustworthiness by detecting incorrect responses.
https://arxiv.org/abs//2405.20003
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
Kernel Language Entropy (KLE) method improves uncertainty quantification in Large Language Models (LLMs) by capturing semantic uncertainty, enhancing trustworthiness by detecting incorrect responses.
https://arxiv.org/abs//2405.20003
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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