[QA] Entropy-Guided Attention for Private LLMs

from Arxiv Papers Hosted by Igor Melnyk

This paper addresses privacy concerns in proprietary language models by optimizing transformer architectures for private inference, focusing on the role of nonlinearities and introducing entropy-guided mechanisms for improved performance. https://arxiv.org/abs//2501.03489 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

Episode details come from the podcast's RSS feed.

Embed this episode

Attribution link and audio player

Ready to play

[QA] Entropy-Guided Attention for Private LLMs

0:00 7:57

No transcript yet.

Requests help us decide what to transcribe next.

Share this episode

Similar podcasts

European Respiratory Journal European Respiratory Society A monthly podcast from the European Respiratory Journal, summarising the research findings of a number of papers published in each issue. GSA on Aging The Gerontological Society of America The Gerontological Society of America (GSA) brings together educators, clinicians, administrators, researchers, and students who share their experiences, expertise, and innovations in aging. The GSA On Aging Podcast series covers a wide range of topics related to aging and gerontology. It features interviews with experts and discussions on research papers published in various GSA journals. The series explores various aspects of aging, including dementia, technology, ageism, age-friendly communities, and other relevant issues affecting older adults.

Frequently asked questions

How long is this episode of Arxiv Papers?

This episode is 7 minutes long.

When was this Arxiv Papers episode published?

This episode was published on January 8, 2025.

Can I download this Arxiv Papers episode?

Yes. Use the download control on the episode player to save the publisher-provided media file.