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Understanding Prompt Tuning and In-Context Learning via Meta-Learning height2pt
The paper explores optimal prompting through a Bayesian perspective, highlighting limitations and advantages of prompt optimization methods, supported by experiments on LSTMs and Transformers.
https://arxiv.org/abs//2505.17010
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
The paper explores optimal prompting through a Bayesian perspective, highlighting limitations and advantages of prompt optimization methods, supported by experiments on LSTMs and Transformers.
https://arxiv.org/abs//2505.17010
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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