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This paper introduces representation engineering (RepE), an approach that enhances transparency in AI systems using insights from cognitive neuroscience. RepE focuses on population-level representations and offers effective solutions for understanding and controlling large language models, addressing safety concerns.
https://arxiv.org/abs//2310.01405
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 representation engineering (RepE), an approach that enhances transparency in AI systems using insights from cognitive neuroscience. RepE focuses on population-level representations and offers effective solutions for understanding and controlling large language models, addressing safety concerns.
https://arxiv.org/abs//2310.01405
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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