Code for What Makes a Language Easy to Deep-learn?

Set up

1. Set up a virtual environment (e.g., via conda) with a recent python version (we used Python 3.9.5)
2. Within the virtual environment, install PyTorch according to your OS, GPU availability, and Python package manager.
3. Within the virtual environment, install all other requirements via pip install -r requirements.txt

Fetch data from experiments with human participants

The data can be obtained via OSF and should be placed in the ./data subfolder. In particular, you need all LearningExp_*_log.txt files and the input_languages.csv file.

Main entry point

The main entry point is train.py. Information on command line arguments can be obtained via python3 train.py -h.

An exemplary command to run an experiment is

    python3 train.py --as_humans /data/path/to/experiment.log --seed 1234 --outdir "results-v1"

Scripts to reproduce experiments

Use the following command to reproduce the main experiments from the paper, sweeping over all experiment log files ten times with different random seeds.

    bash sweep_as_humans.bash

Results will be stored in a subfolder results-v1.

Experiments with GPT-3

The main file for running our experiments with GPT-3 is lang2prompt.py. It expects data directory to be present and filled and will write its outputs to gpt3-completions. You need to specify a language id (S1,B1,S2,...,S5,B5) as a command line argument.

An example call to run the memorization test and the generalization test on language B4 would be:

    python3 lang2prompt.py B4 --gpt3-mem-test --gpt3-reg-test

Important: You need to make sure that the shell environment variable OPENAI_API_KEY holds your API key and edit the line starting with openai.organization with your corresponding organization id.

    python3 lang2prompt.py B4 --gpt3-mem-test --gpt3-reg-test

Run statistics

Use the following command to reproduce the statistical analysis.

    python3 stats.py -o stats-output results-v1