Code for ACL'2021 paper WARP 🌀 Word-level Adversarial ReProgramming

Last update: Nov 06, 2022

Overview

🌀 WARP: Word-level Adversarial ReProgramming

This repository contains code for ACL'2021 Paper WARP: Word-level Adversarial ReProgramming.

^{WARP adds a few trainable embeddings around the input, which causes the masked language model to predict the sentiment of the sentence in the SST-2 task.}

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.

Few-Shot Results

Set	Model	CB		RTE
Set	Model	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
	WARP_init (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
	WARP_init (ALBERT)	70.2	82.4	69.1

^{Results on SuperGLUE benchmark. The results for the test set are obtained from SuperGLUE evaluation server. We only show systems performing in a similar few-shot training setup using 32 examples.}

Setup

The code requires YerevaNN's internal version of allennlp

git clone https://github.com/YerevaNN/allennlp
git checkout warp
pip install .

Training

Linear Probing

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
done

WARP_0

"], "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 done ">

for DATASET in 'cola' 'sst2' 'mrpc' 'qqp' 'stsb' 'mnli' 'rte' 'wnli' 'qnli'
do
    export HPARAMS='{
        "dataset": "'$DATASET'",
        "lr": 0.0001,
        "num_epochs": 20,
        "prompts": [null, "
   
    "],
   
        "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
done

Training WARP

", "prompts":[-10,-11,-12,-13,-14,null,-15,-16,-17,-18,-19,"
",-20,-21,-22,-23,-24,null,-25,-26,-27,-28,-29], "seed":1, "transformer_model":"roberta-large" }' python -m allennlp train \ -s .aim/t-${DATASET} configs/warp.jsonnet ">
export DATASET="rte"
export HPARAMS='{
    "benchmark":"super_glue",
    "classifier_init":null,
    "dataset":"'$DATASET'",
    "ensure_whitespace_between":false,
    "lr":0.001,
    "max_batch_size":8,
    "max_tokens_sq":262144,
    "num_epochs":30,
    "prompt_better_init":"
    
     ",
    
    "prompts":[-10,-11,-12,-13,-14,null,-15,-16,-17,-18,-19,"
    
     ",-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.jsonnet

WARP_init

Few-Shot Experiments

", [-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.jsonnet ">

export 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, "?"], "
   
    ", [-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.jsonnet

",[-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.jsonnet ">

export 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,"?"],"
   
    ",[-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.jsonnet

Evaluation

python -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/test

python -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_matched

Citation

If 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"
}

Code for ACL'2021 paper WARP 🌀 Word-level Adversarial ReProgramming

Related tags

Overview

🌀 WARP: Word-level Adversarial ReProgramming

Few-Shot Results

Setup

Training

Linear Probing

WARP_0

Training WARP

WARP_init

Few-Shot Experiments

Evaluation

Citation

Owner

YerevaNN

Ppq - A powerful offline neural network quantization tool with custimized IR

Animatable Neural Radiance Fields for Modeling Dynamic Human Bodies

SANet: A Slice-Aware Network for Pulmonary Nodule Detection

LF-YOLO (Lighter and Faster YOLO) is used to detect defect of X-ray weld image.

Simple Tensorflow implementation of "Adaptive Convolutions for Structure-Aware Style Transfer" (CVPR 2021)

A bare-bones Python library for quality diversity optimization.

thundernet ncnn

Code and models for "Rethinking Deep Image Prior for Denoising" (ICCV 2021)

source code for 'Finding Valid Adjustments under Non-ignorability with Minimal DAG Knowledge' by A. Shah, K. Shanmugam, K. Ahuja

Coursera - Quiz & Assignment of Coursera

Official PyTorch implementation of "ArtFlow: Unbiased Image Style Transfer via Reversible Neural Flows"

PyTorch implementations of Generative Adversarial Networks.

A program that can analyze videos according to the weights you select

領域を指定し、キーを入力することで画像を保存するツールです。クラス分類用のデータセット作成を想定しています。

An image processing project uses Viola-jones technique to detect faces and then use SIFT algorithm for recognition.

NLP made easy

MTCNN face detection implementation for TensorFlow, as a PIP package.

Solution to the Weather4cast 2021 challenge

PyTorch code for EMNLP 2021 paper: Don't be Contradicted with Anything! CI-ToD: Towards Benchmarking Consistency for Task-oriented Dialogue System

torchbearer: A model fitting library for PyTorch