Text Perturbation Robustness Eval

text-perturbation-robustness-eval

On Robustness of Finetuned Transformer-based NLP Models — Neerudu et al. (2023) (arXiv:2305.14453, 2023)

What this evaluates

Evaluates the robustness of finetuned transformer models (BERT, GPT-2, T5) to various text perturbations (e.g., dropping nouns/verbs, character changes, adding text) across classification and generation tasks. It measures how much model performance degrades when inputs are syntactically or semantically altered.

Datasets

• GLUE — total ?; splits: test (-1); HF glue
• XSum — total ?; splits: test (-1); HF xsum
• CommonGen — total ?; splits: val (-1); HF allenai/common_gen
• SQuAD — total ?; splits: test (-1); HF squad

Metrics

• Accuracy (primary) — range: [0, 1]
  • Fraction of correctly predicted labels out of total instances.
• Matthews CC — range: [-1, 1]
  • Matthews Correlation Coefficient for binary classification tasks (e.g., CoLA).
• Pearson CC — range: [-1, 1]
  • Pearson Correlation Coefficient for regression/similarity tasks (e.g., STS-B).
• ROUGE-1 — range: [0, 1]
  • Unigram overlap F1 score between generated and reference text.
• ROUGE-2 — range: [0, 1]
  • Bigram overlap F1 score between generated and reference text.
• ROUGE-L — range: [0, 1]
  • Longest common subsequence F1 score between generated and reference text.
• Robustness Score (primary) — range: [0, 1]
  • Ratio of the perturbed task metric to the clean (unperturbed) task metric: metric_perturbed / metric_clean.

Input / output format

Input: Single sentences or sentence pairs for classification tasks; source documents for generation tasks.

Output: Predicted class labels or similarity scores for classification; generated text sequences for generation.

Scoring recipe

def compute_robustness(clean_metric, perturbed_metric):
    return perturbed_metric / clean_metric

def compute_task_metric(predictions, gold, metric_name):
    if metric_name == 'Accuracy':
        return sum(p == g for p, g in zip(predictions, gold)) / len(gold)
    elif metric_name == 'Matthews CC':
        return matthews_corrcoef(gold, predictions)
    elif metric_name == 'Pearson CC':
        return pearsonr(gold, predictions)[0]
    elif metric_name.startswith('ROUGE'):
        return rouge_score(gold, predictions, rouge_types=[metric_name])

Common pitfalls

• Robustness scores are reported as ratios (perturbed/clean), not absolute differences or percentages.
• GLUE sub-tasks use different primary metrics (Accuracy, Matthews CC, Pearson CC) that must be applied correctly per dataset.
• CKA and STIR measure representation similarity, not task performance robustness, and should not be confused with the evaluation metrics.

Evidence (verbatim from paper)

Table 1: Accuracy comparison on various GLUE datasets for BERT, GPT-2 and T5 on the test dataset. Table 3: Comparison of robustness scores on various GLUE tasks for finetuned Transformer models under different types of perturbations.

Citation

@misc{neerudu2023robustness,
  title={On Robustness of Finetuned Transformer-based NLP Models},
  author={Neerudu et al. (2023)},
  year={2023},
  note={arXiv:2305.14453}
}

• arXiv: 2305.14453