InstructZero: Efficient Instruction Optimization for Black-Box Large
  Language Models
Large language models~(LLMs) are instruction followers, but it can be
challenging to find the best instruction for different situations, especially
for black-box LLMs on which backpropagation is forbidden. Instead of directly
optimizing the discrete instruction, we optimize a low-dimensional soft prompt
applied to an open-source LLM to generate the instruction for the black-box
LLM. On each iteration of the proposed method, which we call InstructZero, a
soft prompt is converted into an instruction using the open-source LLM, which
is then submitted to the black-box LLM for zero-shot evaluation, and the
performance is sent to Bayesian optimization to produce new soft prompts
improving the zero-shot performance. We evaluate InstructZero on different
combinations of open-source LLMs and APIs including Vicuna and ChatGPT. Our
results show that InstructZero outperforms SOTA auto-instruction methods across
a variety of downstream tasks. Our code and data are publicly available at
https://github.com/Lichang-Chen/InstructZero.
647e877fe4d52fe0e0210f88
Lichang Chen
claimed_verified
2023-06-06T17:23:06.023Z
62323bb408bcea92917e42ee
https://cdn-avatars.huggingface.co/v1/production/uploads/62323bb408bcea92917e42ee/2vHxkv-oSROtLteOnqa8P.jpeg
Lichang Chen
user
Lichang-Chen
647e877fe4d52fe0e0210f89
Jiuhai Chen
647e877fe4d52fe0e0210f8a
Tom Goldstein
admin_assigned
2023-06-06T09:59:12.898Z
6381ca7d65dc156aba0b933d
/avatars/84dfdca8e1cd6fbf50d6fb2a6f1b488d.svg
Tom Goldstein
user
tomgoldstein
647e877fe4d52fe0e0210f8b
Heng Huang
647e877fe4d52fe0e0210f8c
Tianyi Zhou
claimed_verified
2023-06-06T17:23:10.958Z
647f5af5b0e96764589f3b2a
https://cdn-avatars.huggingface.co/v1/production/uploads/noauth/VJ4cDyjp5M3V5WmI5gPIU.jpeg
Tianyi Zhou
user
zhoutianyi
InstructZero: Efficient Instruction Optimization for Black-Box Large
  Language Models
Large language models~(LLMs) are instruction followers, but it can be
challenging to find the best instruction for different situations, especially
for black-box LLMs on which backpropagation is forbidden. Instead of directly
optimizing the discrete instruction, we optimize a low-dimensional soft prompt
applied to an open-source LLM to generate the instruction for the black-box
LLM. On each iteration of the proposed method, which we call InstructZero, a
soft prompt is converted into an instruction using the open-source LLM, which
is then submitted to the black-box LLM for zero-shot evaluation, and the
performance is sent to Bayesian optimization to produce new soft prompts
improving the zero-shot performance. We evaluate InstructZero on different
combinations of open-source LLMs and APIs including Vicuna and ChatGPT. Our
results show that InstructZero outperforms SOTA auto-instruction methods across
a variety of downstream tasks. Our code and data are publicly available at
https://github.com/Lichang-Chen/InstructZero.
InstructZero optimizes a soft prompt to generate instructions for black-box LLMs, improving zero-shot performance using Bayesian optimization.
large language models LLMs zero-shot evaluation Bayesian optimization soft prompt instruction generation Vicuna ChatGPT