Instruction Induction

This repository contains the code and data of the paper: "Instruction Induction: From Few Examples to Natural Language Task Descriptions"

Data

The data for the instruction induction experiments, as well as for the execution accuracy evaluation, are available in the data folder.

Usage

Setup

Install the required packages using pip install -r requirements.txt.

Instructions Prediction

To run the instruction induction experiments, run the following command:

python induction.py \
--engine $OPENAI_ENGINE \
--organization $OPENAI_ORGANIZATION \
--api_key $OPENAI_API_KEY \
--data_dir $INPUT_DATA_DIR \
--out_dir $OUTPUT_DIR \
--max_tokens $MAX_TOKENS \
--tasks $TASK_LIST

where

$OPENAI_ENGINE is the model used for inducing instructions (default: text-davinci-002).
$OPENAI_ORGANIZATION is your OpenAI API organization.
$OPENAI_API_KEY is your OpenAI API key.
$INPUT_DATA_DIR is a path to the input data, should be in the format specified in data/induction_input (default: data/induction_input)
$OUTPUT_DIR is the output dir path, will contain the predictions.
$MAX_TOKENS is an upper bound on how many tokens the model can generate - max_tokens in the OpenAI API (default: 50).
$TASK_LIST is a list of all tested tasks. Task names should correspond to the input files in $INPUT_DATA_DIR. Defaults to all tasks under data/induction_input.

Postprocessing

We apply a postprocessing protocol, which includes a basic cleanup for the generated instructions as well as grouping identical instructions, to speedup and reduce the cost of the execution accuracy experiments. To postprocess the generated instructions, run

python postprocess_instructions.py \
--engine $OPENAI_ENGINE \
--predictions_dir $PREDICTIONS_DIR \
--tasks $TASK_LIST

where

$OPENAI_ENGINE is the name of the model that was used for inducing instructions (default: text-davinci-002).
$PREDICTIONS_DIR is a path to a directory containing the predictions (the out_dir) passed to the induction script.
$TASK_LIST is a list of all tested tasks. Task names should correspond to the input files in $PREDICTIONS_DIR. Defaults to all the instruction induction tasks.

Evaluation

To measure the execution accuracy of the generated instructions, first run the following command:

python prepare_for_execution.py \
--model_name $OPENAI_ENGINE \
--execute_data_dir $EXECUTE_DATA_DIR \
--predictions_dir $PREDICTIONS_DIR \
--out_dir $OUTPUT_DIR \
--tasks $TASK_LIST

where

$OPENAI_ENGINE is the name of the model that was used for inducing instructions (default: text-davinci-002).
$EXECUTE_DATA_DIR is the path of the (without instructions) execution set (default: data/raw/execute).
$PREDICTIONS_DIR is a path of a directory containing the predictions (after postprocessing).
$OUTPUT_DIR will contain the execution accuracy experiment inputs.
$TASK_LIST is a list of all evaluated tasks. Task names should correspond to the input files in $INPUT_DATA_DIR. Defaults to all tasks under data/induction_input.

Next, to execute the instructions, run

python execute_instructions.py \
--execution_engine $OPENAI_EXECUTION_ENGINE \
--instruction_generation_model $INSTRUCTION_GENERATION_MODEL \
--organization $OPENAI_ORGANIZATION \
--api_key $OPENAI_API_KEY \
--input_dir $INPUT_DATA_DIR \
--out_dir $OUTPUT_DIR \
--max_tokens $MAX_TOKENS \
--tasks $TASK_LIST

where

$OPENAI_EXECUTION_ENGINE is the model that will be used for executing the instructions (default: text-davinci-002).
$INSTRUCTION_GENERATION_MODEL is the evaluated model - the model that was used to generate instructions (default: text-davinci-002).
$OPENAI_ORGANIZATION is your OpenAI API organization.
$OPENAI_API_KEY is your OpenAI API key.
$INPUT_DATA_DIR is a path of the input execution accuracy data.
$OUTPUT_DIR is the output dir path, will contain the execution accuracy predictions.
$MAX_TOKENS is an upper bound on how many tokens the model can generate - max_tokens in the OpenAI API (default: 30).
$TASK_LIST is a list of all tested tasks. Task names should correspond to the input files in $INPUT_DATA_DIR. Defaults to all tasks under data/induction_input.

Finally, to obtain the execution accuracy scores, run the following command:

python evaluate.py \
--instruction_generation_model $INSTRUCTION_GENERATION_MODEL \
--execution_input_dir $INPUT_DATA_DIR \
--predictions_dir $PREDICTIONS_DIR \
--tasks $TASK_LIST

where

$INSTRUCTION_GENERATION_MODEL is the evaluated model - the model that was used to generate instructions (default: text-davinci-002).
$INPUT_DATA_DIR is a path of the input execution accuracy data.
$PREDICTIONS_DIR is a path containing the instructions execution outputs.
$TASK_LIST is a list of all tested tasks. Task names should correspond to the input files in $INPUT_DATA_DIR. Defaults to all tasks under data/induction_input.

Citation

@misc{honovich2022induction,
      title={Instruction Induction: From Few Examples to Natural Language Task Descriptions},
      author={Honovich, Or and Shaham, Uri and Bowman, Samuel R. and Levy, Omer},
      year={2022},
      eprint={2205.10782},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}

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