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The paper questions the effectiveness of direct model editing for correcting factual errors in language models. It suggests alternative approaches such as retrieval-based architectures, concept erasure methods, and attribution methods, and emphasizes the need for cautious use of model editing in language model deployment.
https://arxiv.org/abs//2310.11958
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
The paper questions the effectiveness of direct model editing for correcting factual errors in language models. It suggests alternative approaches such as retrieval-based architectures, concept erasure methods, and attribution methods, and emphasizes the need for cautious use of model editing in language model deployment.
https://arxiv.org/abs//2310.11958
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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