This repository contains code for ACL'2021 Paper WARP: Word-level Adversarial ReProgramming.
Transfer learning from pretrained language models recently became the dominant approach for solving many NLP tasks. A common approach to transfer learning for multiple tasks that maximize parameter sharing trains one or more task-specific layers on top of the language model.
In this paper, we present an alternative approach based on adversarial reprogramming, which extends earlier work on automatic prompt generation. Adversarial reprogramming attempts to learn task-specific word embeddings that, when concatenated to the input text, instruct the language model to solve the specified task.
Using up to 25K trainable parameters per task, this approach outperforms all existing methods that use up to 25M trainable parameters on the public leaderboard of the GLUE benchmark. Our method, initialized with task-specific human-readable prompts, also works in a few-shot setting, outperforming GPT-3 on two SuperGLUE tasks after training on just 32 samples.
| Set | Model | CB | RTE | |
|---|---|---|---|---|
| F1 | Acc. | Acc. | ||
| dev | ||||
| GPT-3 Small | 26.1 | 42.9 | 52.3 | |
| GPT-3 Med | 40.4 | 58.9 | 48.4 | |
| GPT-3 | 57.2 | 82.1 | 72.9 | |
| PET (ALBERT) | 59.4 | 85.1 | 69.8 | |
| iPET (ALBERT) | 92.4 | 92.9 | 74.0 | |
| WARPinit (ALBERT) | 84.0 | 87.5 | 71.8 | |
| test | ||||
| GPT-3 | 52.0 | 75.6 | 69.0 | |
| PET (ALBERT) | 60.2 | 87.2 | 67.2 | |
| iPET (ALBERT) | 79.9 | 88.8 | 70.8 | |
| WARPinit (ALBERT) | 70.2 | 82.4 | 69.1 | |
The code requires YerevaNN's internal version of allennlp
git clone https://github.com/YerevaNN/allennlp
git checkout warp
pip install .
for DATASET in 'cola' 'sst2' 'mrpc' 'qqp' 'stsb' 'mnli' 'rte' 'wnli' 'qnli'
do
export HPARAMS='{
"dataset": "'$DATASET'",
"lr": 0.0001,
"num_epochs": 20,
"prompts": [],
"reorder_optimized": false,
"max_batch_size": 8,
"max_tokens_sq": 262144, "on_logits": false, "pooling_index": null, "seed": 1}'
python -m allennlp train \
-s .aim/baseline-linear-${DATASET} configs/warp.jsonnet
donefor DATASET in 'cola' 'sst2' 'mrpc' 'qqp' 'stsb' 'mnli' 'rte' 'wnli' 'qnli'
do
export HPARAMS='{
"dataset": "'$DATASET'",
"lr": 0.0001,
"num_epochs": 20,
"prompts": [null, "<mask>"],
"reorder_optimized": true,
"max_batch_size": 8,
"max_tokens_sq": 262144,
"on_logits": "pre_decoder_layer_norm",
"pooling_index": 1,
"seed": 1
}'
python -m allennlp train \
-s .aim/baseline-warp_0-${DATASET} configs/warp.jsonnet
doneexport DATASET="rte"
export HPARAMS='{
"benchmark":"super_glue",
"classifier_init":null,
"dataset":"'$DATASET'",
"ensure_whitespace_between":false,
"lr":0.001,
"max_batch_size":8,
"max_tokens_sq":262144,
"num_epochs":30,
"prompt_better_init":"<mask>",
"prompts":[-10,-11,-12,-13,-14,null,-15,-16,-17,-18,-19,"<mask>",-20,-21,-22,-23,-24,null,-25,-26,-27,-28,-29],
"seed":1,
"transformer_model":"roberta-large"
}'
python -m allennlp train \
-s .aim/t-${DATASET} configs/warp.jsonnetexport HPARAMS='{
"benchmark":"super_glue",
"classifier_init": {
"entailment": " yes",
"not_entailment": " instead"
},
"dataset":"few_rte",
"eval_mode":false,
"lr":0.001,
"max_batch_size":2,
"max_tokens_sq":131072,
"num_epochs":100,
"num_gradient_accumulation_steps":2,
"prompt_better_init": "[PAD]",
"prompts":[-10,-11,[-14,"\""],null,[-15,"\""], [-16, "?"], "<mask>", [-20, ","], null, [-29, "!"],-30,-31],
"seed":3,
"str_cut_frac":0,
"transformer_model":"albert-xxlarge-v2",
"validation_metric": null
}'
python -m allennlp train \
-s .aim/t-${DATASET}-`date +%s` configs/warp.jsonnetexport HPARAMS='{
"benchmark":"super_glue",
"classifier_init":{
"entailment":" yes",
"not_entailment":" instead"
},
"dataset":"few_rte",
"grad_norm":1,
"lr":0.001,
"max_batch_size":2,
"max_tokens_sq":131072,
"num_epochs":30,
"num_gradient_accumulation_steps":2,
"prompt_better_init":"[PAD]",
"prompts":[-10,-11,[-14,"\""],null,[-15,"\""],[-16,"?"],"<mask>",[-20,","],null,[-29,"!"],-30,-31],
"seed":1,
"str_cut_frac":0.06,
"transformer_model":"albert-xxlarge-v2",
"validation_metric":"+training_val_metric"
}'
python -m allennlp train \
-s .aim/t-${DATASET}-`date +%s` configs/warp.jsonnetpython -m allennlp predict \
--silent --use-dataset-reader --cuda-device 0 \
--batch-size 50 \
--predictor glue --output-file v0.1/AX.tsv /data/arp/.aim/H-93ae5ae9 ax/testpython -m allennlp predict \
--silent --use-dataset-reader --cuda-device 0 \
--batch-size 50 \
--predictor glue --output-file v0.1/MNLI-m.tsv /data/arp/.aim/H-93ae5ae9 test_matchedIf you want to refer to our work use this bibTeX:
@inproceedings{hambardzumyan-etal-2021-warp,
title = "{WARP}: {W}ord-level {A}dversarial {R}e{P}rogramming",
author = "Hambardzumyan, Karen and
Khachatrian, Hrant and
May, Jonathan",
booktitle = "Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)",
month = aug,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.acl-long.381",
doi = "10.18653/v1/2021.acl-long.381",
pages = "4921--4933"
}
React
Typescript
Django
Laravel
Facebook
Microsoft
Google
Alibaba
D3
Tencent