A Bayesian approach for prompt optimization in pre-trained language models

1 December 2023

Papers citing "A Bayesian approach for prompt optimization in pre-trained language models"

8 / 8 papers shown

Title
Reliable Gradient-free and Likelihood-free Prompt Tuning Maohao Shen S. Ghosh P. Sattigeri Subhro Das Yuheng Bu G. Wornell VLM 46 10 0 30 Apr 2023
Bayesian Optimization over High-Dimensional Combinatorial Spaces via Dictionary-based Embeddings Aryan Deshwal Sebastian Ament Maximilian Balandat E. Bakshy J. Doppa David Eriksson 48 19 0 03 Mar 2023
BBTv2: Towards a Gradient-Free Future with Large Language Models Tianxiang Sun Zhengfu He Hong Qian Yunhua Zhou Xuanjing Huang Xipeng Qiu 108 53 0 23 May 2022
Local Latent Space Bayesian Optimization over Structured Inputs Natalie Maus Haydn Thomas Jones Juston Moore Matt J. Kusner John Bradshaw Jacob R. Gardner BDL 51 69 0 28 Jan 2022
P-Tuning v2: Prompt Tuning Can Be Comparable to Fine-tuning Universally Across Scales and Tasks Xiao Liu Kaixuan Ji Yicheng Fu Weng Lam Tam Zhengxiao Du Zhilin Yang Jie Tang VLM 238 806 0 14 Oct 2021
The Power of Scale for Parameter-Efficient Prompt Tuning Brian Lester Rami Al-Rfou Noah Constant VPVLM 280 3,848 0 18 Apr 2021
What Makes Good In-Context Examples for GPT- $3$ ? Jiachang Liu Dinghan Shen Yizhe Zhang Bill Dolan Lawrence Carin Weizhu Chen AAML RALM 275 1,312 0 17 Jan 2021
GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding Alex Jinpeng Wang Amanpreet Singh Julian Michael Felix Hill Omer Levy Samuel R. Bowman ELM 297 6,959 0 20 Apr 2018