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Do Prompt-Based Models Really Understand the Meaning of Their Prompts?


This repository accompanies our paper “Do Prompt-Based Models Really Understand the Meaning of Their Prompts?”

Usage

To replicate our results in Section 4, run:

python3 prompt_tune.py 
    --save-dir ../runs/prompt_tuned_sec4/ 
    --prompt-path ../data/binary_NLI_prompts.csv 
    --experiment-name sec4 
    --few-shots 3,5,10,20,30,50,100,250 
    --production 
    --seeds 1

Add --fully-train if you want to train on the entire training set in addition to few-shot settings.

To replicate Section 5, run:

python3 prompt_tune.py 
    --save-dir ../runs/prompt_tuned_sec5/ 
    --prompt-path ../data/binary_NLI_prompts_permuted.csv 
    --experiment-name sec5 
    --few-shots 3,5,10,20,30,50,100,250 
    --production 
    --seeds 1

To get a fine-tuning baseline (Figure 1):

python3 fine_tune.py 
    --save-dir ../runs/fine_tune/ 
    --epochs 5 
    --few-shots 3,5,10,20,30,50,100,250 
    --fully-train 
    --production 
    --seeds 1

To replicate our exact results, use --seeds 1,2,3,4,5,6,7,8, which yields starting_example_index of 550,231,974,966,1046,2350,1326,928 respectively. This is important for ensuring that all models trained under the same seed always see exactly the same training examples. See paper Section 3 for more details.

If these seeds do not generate the same starting_example_index for you (which you can check in the output CSV files), you will have to manually specify the few-shot subset of training examples. I plan to add an argparse argument for this to make it easy.

All other hyperparameters are the same as the argparse default.

Miscellaneous Notes

You might notice that the code and output files are set up to produce a fine-grained analysis of HANS (McCoy et al., 2019). We actually run all of our main experiments on HANS as well and got similar results, which we plan to write up in a future version of our paper. Meanwhile, if you’re curious, feel free to add --do-diagnosis which will report the results on HANS.

Requirements

Python 3.9.

3.7 should mostly work too. You’d have to just replace the new built-in type hints and dictionary union operators with their older equivalents.

Activate your preferred virtual envrionment and then run pip install -r requirements.txt. If you want to replicate our exact results, use

torch==1.9.0+cu111
transformers==4.9.2
datasets==1.11.0

GitHub

https://github.com/awebson/prompt_semantics


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