Understanding prompt engineering may not require rethinking
generalization

Understanding prompt engineering may not require rethinking generalization

6 October 2023

Victor Akinwande

J. Zico Kolter

Papers citing "Understanding prompt engineering may not require rethinking generalization"

8 / 8 papers shown

Title
Automated Black-box Prompt Engineering for Personalized Text-to-Image Generation Yutong He Alexander Robey Naoki Murata Yiding Jiang J. Williams George Pappas Hamed Hassani Yuki Mitsufuji Ruslan Salakhutdinov J. Zico Kolter DiffM 67 4 0 28 Mar 2024
PromptSource: An Integrated Development Environment and Repository for Natural Language Prompts Stephen H. Bach Victor Sanh Zheng-Xin Yong Albert Webson Colin Raffel ... Khalid Almubarak Xiangru Tang Dragomir R. Radev Mike Tian-Jian Jiang Alexander M. Rush VLM 205 274 0 02 Feb 2022
Learning to Prompt for Vision-Language Models Kaiyang Zhou Jingkang Yang Chen Change Loy Ziwei Liu VPVLM CLIP VLM 308 2,108 0 02 Sep 2021
The Power of Scale for Parameter-Efficient Prompt Tuning Brian Lester Rami Al-Rfou Noah Constant VPVLM 265 2,999 0 18 Apr 2021
Scaling Up Visual and Vision-Language Representation Learning With Noisy Text Supervision Chao Jia Yinfei Yang Ye Xia Yi-Ting Chen Zarana Parekh Hieu H. Pham Quoc V. Le Yun-hsuan Sung Zhen Li Tom Duerig VLM CLIP 283 2,875 0 11 Feb 2021
Making Pre-trained Language Models Better Few-shot Learners Tianyu Gao Adam Fisch Danqi Chen 225 1,649 0 31 Dec 2020
Scaling Laws for Neural Language Models Jared Kaplan Sam McCandlish T. Henighan Tom B. Brown B. Chess R. Child Scott Gray Alec Radford Jeff Wu Dario Amodei 215 3,054 0 23 Jan 2020
Norm-Based Capacity Control in Neural Networks Behnam Neyshabur Ryota Tomioka Nathan Srebro 86 551 0 27 Feb 2015