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This paper introduces Woodpecker, a training-free method to mitigate hallucinations in Multimodal Large Language Models. It corrects hallucinations in generated text by extracting key concepts, validating visual knowledge, and generating visual claims. Woodpecker shows promising results on benchmark tests.
https://arxiv.org/abs//2310.16045
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
By Igor Melnyk5
33 ratings
This paper introduces Woodpecker, a training-free method to mitigate hallucinations in Multimodal Large Language Models. It corrects hallucinations in generated text by extracting key concepts, validating visual knowledge, and generating visual claims. Woodpecker shows promising results on benchmark tests.
https://arxiv.org/abs//2310.16045
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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