Instance Attribution Eval

instance-attribution-eval

An Empirical Comparison of Instance Attribution Methods for NLP — Pezeshkpour et al. (2021) (arXiv:2104.04128, 2021)

What this evaluates

Evaluates the ability of different instance attribution methods to rank training data instances by their influence on a given test prediction, particularly focusing on identifying problematic training artifacts and comparing gradient-based versus similarity-based approaches.

Datasets

• SST-2 — total ?; splits: test (-1)
• MNLI — total ?; splits: train (10000), test (-1)
• HANS — total 1000; splits: test (1000)

Metrics

• Spearman Correlation (primary) — range: [-1, 1]
  • Measures the rank correlation between the importance scores assigned to training instances by two different attribution methods. Computed as the Pearson correlation coefficient between the rank-transformed scores of the training set.

Input / output format

Input: A target test instance, a trained model (BERT-based), and the training dataset (or a sampled subset).

Output: A ranked list or continuous importance score for each training instance relative to the target test instance.

Scoring recipe

def compute_spearman(scores_a, scores_b):
    rank_a = rankdata(scores_a)
    rank_b = rankdata(scores_b)
    n = len(rank_a)
    d_sq = sum((r1 - r2) ** 2 for r1, r2 in zip(rank_a, rank_b))
    return 1.0 - (6.0 * d_sq) / (n * (n ** 2 - 1))

Common pitfalls

• Computing full Influence Functions for BERT is infeasible due to Hessian storage requirements (~12 PB), so approximations like LiSSa or parameter subsets (Top-5 layers, linear layer) must be used.
• The LiSSa approximation for the Inverse Hessian Vector Product is sensitive to the norm of the approximation, often requiring a large scaling factor for convergence and becoming unstable with additional layers.

Evidence (verbatim from paper)

The similarity between influence of training samples for different pairs of attribution methods on the SST and MNLI datasets was measured via Spearman Correlation.

Citation

@misc{pezeshkpour2021instance,
  title={An Empirical Comparison of Instance Attribution Methods for NLP},
  author={Pezeshkpour et al. (2021)},
  year={2021},
  note={arXiv:2104.04128}
}

• arXiv: 2104.04128