DExperts
Hi! This repository contains code for the paper DExperts: Decoding-Time Controlled Text Generation with Experts and Anti-Experts to appear at ACL 2021. If you have any questions, please feel free to create a Github issue or reach out to the first author at [email protected].
Create a conda environment called dexperts with
conda env create -f environment.yml
Toxicity
To generate continuations with DExperts and score them for toxicity using the PerspectiveAPI toxicity scorer, run the following command.
OUTPUT_DIR=generations/toxicity/dexperts
PROMPTS_DATASET=prompts/nontoxic_prompts-10k.jsonl
python -m scripts.run_toxicity_experiment \
--use-dataset \
--dataset-file $PROMPTS_DATASET \
--model-type dexperts \
--model gpt2-large \
--nontoxic-model $MODEL_DIR/finetuned_gpt2_nontoxic \
--toxic-model $MODEL_DIR/finetuned_gpt2_toxic \
--perspective-rate-limit $API_RATE \
--alpha 2.0 \
--filter_p 0.9 \
$OUTPUT_DIR
In general, model_type is one of gpt2 (the base model), dexperts (our method), and pplm. With an OpenAI API key for GPT-3 access, you can also try gpt3 and dexperts-gpt3. Different methods have different additional parameters to specify; to see the commands we used for each method in our paper, please look under scripts/our_scripts/toxicity. For experiments with GeDi, we directly used the original authors' codebase.
When model_type is dexperts, we can steer away from toxicity using only a toxic anti-expert. To do this, leave --nontoxic-model empty, and DExperts will re-use the base model as the expert. The hyperparameter alpha controls the strength of steering over the base model. We use filter_p to use the nucleus from the base model, as described in Section 2.2 of our paper.
This script will create three files in OUTPUT_DIR: generations.jsonl with all of the generated continuations, perspective.jsonl with all the scores from Perspective API, and prompted_gens_[model_type].jsonl, which collates the previous two files.
To try a model's output on your own prompts, simply create your own prompts file! To see the format of the prompts file, see prompts/toy_prompt.jsonl.
Sentiment
To generate continuations with DExperts conditioned on sentiment prompts and score them for sentiment using HuggingFace's sentiment classifier, run the following command.
PROMPTS_DATASET=prompts/sentiment_prompts-10k/neutral_prompts.jsonl
OUTPUT_DIR=generations/sentiment/neutral_prompts/dexperts/positive/
python -m scripts.run_sentiment_experiment \
--use-dataset \
--dataset-file $PROMPTS_DATASET \
--model-type dexperts \
--model gpt2-large \
--pos-model $MODEL_DIR/finetuned_gpt2_positive \
--neg-model $MODEL_DIR/finetuned_gpt2_negative \
--alpha 3.2 \
--filter_p 0.9 \
$OUTPUT_DIR
The model_type can be any of the options from before, with the addition of ctrl. Again, the full commands used for each method can be found under scripts/our_scripts/sentiment.
When model_type is dexperts, we always interpret --pos-model as the expert and --neg-model as the anti-expert; for negative steering, use alpha < 0. By leaving one of --pos-model or --neg-model empty, DExperts will re-use the base model as the missing expert or anti-expert.
Evaluation
To evaluate generated output for fluency and diversity, run the following command. The GENERATIONS_FILE should have the format prompted_gens_[model_type].jsonl.
python -m scripts.evaluation.evaluate_generations \
--generations_file $GENERATIONS_FILE
Notebooks
Our jupyter notebooks are in notebooks/. To obtain the same tables and plots that appear in the paper, look in sentiment_results.ipynb, toxicity_results.ipynb, and human_eval_results.ipynb. To create your own prompts dataset with a couple lines of code, you can get started with prompts_playground.ipynb. Sample and compare generations from each model with review_sentiment_generations.ipynb and review_toxicity_generations.ipynb.
Downloading the original data and models from our paper
To download the prompts we used for evaluation, generations output by each model, and finetuning datasets from our paper, ensure you have gdown installed, then run the following commands inside the dexperts/ root directory. Descriptions of the contents of each of these folders can be found within the folder.
# prompts
gdown https://drive.google.com/uc?id=1bI49aJvmEoLdqSNb30JkORdsNJmv7Aep
unzip prompts.zip && rm prompts.zip
# generations
gdown https://drive.google.com/uc?id=10jL1-eCv8w3oeGFgA_jrel0enrNVdFW7
unzip generations.zip && rm generations.zip
# datasets
gdown https://drive.google.com/uc?id=1MeEjLPxQ77AYtzL0nd1hYJTlL8OJgHkI
unzip datasets.zip && rm datasets.zip
To download models from our paper,
mkdir models
cd models
# (anti-)expert models
gdown https://drive.google.com/uc?id=1HSrNMrq4OZ3nyTobNd2TZFcB5NYwluu-
unzip experts.zip && rm experts.zip
# DAPT models
gdown https://drive.google.com/uc?id=1eDlRU04s-H1elWWtPuDoBNAqyoqj3_p9
unzip dapt.zip && rm dapt.zip
# PPLM classifiers
gdown https://drive.google.com/uc?id=17s26QM9vJp9hCUkRBrDx5Wa__4BlrqGL
unzip pplm_classifiers.zip && rm pplm_classifiers.zip
Citation
@inproceedings{liu-etal-2021-dexperts,
title = "{DExperts}: Decoding-Time Controlled Text Generation with Experts and Anti-Experts",
author = "Alisa Liu and Maarten Sap and Ximing Lu and Swabha Swayamdipta and Chandra Bhagavatula and Noah A. Smith and Yejin Choi",
booktitle = "Proceedings of the Joint Conference of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (ACL-IJCNLP)",
year = "2021",
url = "https://arxiv.org/abs/2105.03023",
}
This code was built on top of allenai/real-toxicity-prompts and with inspiration from yangkevin2/naacl-2021-fudge-controlled-generation.
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