Code for Explanation Selection Using Unlabeled Data for Chain-of-Thought Prompting (EMNLP 2023).
- python==3.8
- requirements:
pip install -r requirements.txt
- Set OPENAI KEY:
export KEY=yourkey
- Run
mkdir misc expl_search/misc expl_search/annotations expl_search/candidates
We directly provide pairs of seed few-shot CoT prompts and searched CoT prompts in prompt pairs. (Note these prompts are optimized for code-davinci-002
).
Run evaluation to compare seed CoTs and optimized CoTs on code-002
SETS="0" sh exp_scripts/exp_result.sh gsm # datasets include gsm, ecqa, esnli, strategyqa
We take GSM as an example dataset
# SEED
sh exp_scripts/main/gsm.sh SEED
# OSACC
sh exp_scripts/main/gsm.sh OSACC
# OSLL
sh exp_scripts/main/gsm.sh OSLL
@InProceedings{Ye-Durrett:2023:explselect,
title = {Explanation Selection Using Unlabeled Data for In-Context Learning},
author = {Xi Ye and Greg Durrett},
booktitle = {Proceedings of EMNLP},
year = {2023},
}