The Unreliability of Explanations in Few-shot Prompting for Textual Reasoning (NeurIPS 2022)

Requirements:

• openai
• transformers
• scikit-learn

Set OPENAI KEY:

export OPENAI_API_KEY=yourkey

Please run the following commands under synth dir.

(1) Running standard few-shot prompting:

python few_shot.py --run_pred --train_slice 0

(2) Runninng explain-predict or predict-explain pipeline:

python joint.py --run_pred --style e-p (p-e) --train_slice 0

(3) Calibrating the predictions of predict-explain: python posthoc_pipeline.py --style p-e --train_slice 0

Arguments

Note that we will store the API query results as json files in synth/misc.

There are some arguements in few_shot.py and joint.py.

• --engine specifies the OPENAI API engine (e.g., davinci,text-davinci-001)
• --style specifies the prompt format style
• --train_slice specifies the random split of the shots in prompts. Because the trainning data is randomly sampled and permuted, different train_slice will result in different randomly sampled shot sets.

Please refer to HotpotAdv/run_exp.py and ESNL/run_exp.py. The commands and arguments are the same as the experiments on Synth.