**Adversarial Fine-Tuning with Data Augmentation for Improve

Adversarial Fine-Tuning with Data Augmentation for Improved Robustness

I'd like to share a unique fine-tuning approach that combines data augmentation and adversarial training to enhance the robustness of Large Language Models (LLMs). The following snippet utilizes the popular Hugging Face library and the transformers package to fine-tune a pre-trained LLM on a dataset with augmented examples and adversarial perturbations:

from transformers import AutoModelForSequenceClassification, AutoTokenizer
import torch
import pandas as pd

# Load pre-trained model and tokenizer
model_name = "bert-base-uncased"
model = AutoModelForSequenceClassification.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Load dataset and create data augmenter
dataset = pd.read_csv("data.csv")
augmenter = TextAugmenter(model_name)  # assume TextAugmenter is a custom class

# Fine-tune model with data augmentation and adversarial perturbations
def fine_tune(model, dataset, augmenter):
    for batch in dataset:
        inputs = tokenizer(batch, return_tensors="pt")
        augmented_inputs = augmenter.augment(inputs)
        inputs = torch.cat((inputs, augmented_inputs))
        outputs = model(inputs)
        # optimize model parameters using the augmented inputs and labels
        loss = F.nll_loss(outputs, labels)
        loss.backward()
        optimizer.step()

fine_tune(model, dataset, augmenter)

This code snippet fine-tunes a pre-trained LLM by incorporating both data augmentation and adversarial training. Specifically:

1. Data Augmentation: The TextAugmenter class creates new examples by applying random transformations to the input text, such as paraphrasing, back translation, and insertion/deletion of words.
2. Adversarial Training: The model is trained using both the original and augmented inputs, which helps it generalize better to unseen examples and defend against adversarial attacks.

By combining these two techniques, this fine-tuning approach enhances the robustness of LLMs and improves their overall performance on real-world tasks.

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