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alex zhang#846
alex zhang#846Stanford's Yoonho Lee argues that text optimization methods like prompting are undervalued compared to gradient-based weight training
The methods offer superior sample efficiency in low-data settings.
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Omar Khattab@lateinteraction
as usual from @yoonholeee, this is extremely well-written and a great way to organize the major arguments in this space
Yoonho Lee@yoonholeee
http://x.com/i/article/2064017981982859264
7hViews 25.3KLikes 205Bookmarks 237
Ahmad Beirami@abeirami
Excellent piece by @yoonholeee!
Harness should be learned, not engineered, and you'll find rich research directions on harness optimization in Yoonhoo's post.
Yoonho Lee@yoonholeee
http://x.com/i/article/2064017981982859264
55mViews 86Likes 0Bookmarks 2
Ahmad Beirami@abeirami
Excellent piece by @yoonholeee!
Harness should be learned, not engineered. You'll find rich research directions on harness optimization in Yoonhoo's post.
Yoonho Lee@yoonholeee
http://x.com/i/article/2064017981982859264
50mViews 151Likes 1Bookmarks 1
oso@osoleve
@yoonholeee @a1zhang I started making Tools For This, glitchlings has tools for corrupting text as well as a starter toolkit for comparative sequence analysis (and lots of integrations), all FL/OSS 🫡
https://github.com/osoleve/glitchlings
10hViews 91Likes 2
Rugbist@rugbist_
@lateinteraction @yoonholeee rare to see someone break it down without taking sides tbh
what stood out to u the most from it?
7hViews 14
Jesse Jr Lim (林振燊)@Jessejrlim
@yoonholeee ^^ I'm building alot of this. Some differences. Improvement has to be gated, versioned for compliance. But there's ways to have best of both worlds. https://chatgpt.com/share/6a2731ea-d2d8-8330-a501-6f8001b7bb43
7hViews 8