Zero Shot Generalization Eval

zero-shot-generalization-eval

Multitask Prompted Training Enables Zero-Shot Task Generalization — Victor Sanh et al. (2021) (arXiv:2110.08207, 2021)

What this evaluates

Evaluates a model's ability to generalize to unseen natural language tasks without task-specific fine-tuning or prompt tuning. It probes zero-shot performance across traditional NLP benchmarks and novel BIG-bench tasks using accuracy.

Datasets

• BIG-bench & Held-out NLP Tasks — total ?; splits: val (-1)

Metrics

• accuracy (primary) — range: [0, 1]
  • Standard classification accuracy: the proportion of correctly predicted options among all test instances. For multiple-choice tasks, predictions are made via rank classification by selecting the option with the highest unnormalized log-likelihood.

Input / output format

Input: Input text (prompt + question/options) fed to an encoder, truncated to 1024 tokens.

Output: Target text (correct answer or option) generated autoregressively by the decoder, truncated to 256 tokens.

Scoring recipe

def compute_accuracy(predictions, gold):
    correct = sum(1 for p, g in zip(predictions, gold) if p == g)
    return correct / len(gold)
# For multiple-choice: prediction = argmax(log_prob(option) for option in options)
# Final report: median accuracy across all prompt templates per dataset.

Common pitfalls

• Selecting the best prompt based on validation performance leaks evaluation data and violates the true zero-shot setting.
• Log-likelihoods for multiple-choice options are not length-normalized, which can bias predictions toward shorter answers.

Evidence (verbatim from paper)

We evaluate zero-shot generalization on 11 datasets in 4 held-out traditional NLP tasks: natural language inference, coreference, word sense disambiguation, and sentence completion, as well as 14 novel tasks from BIG-bench (§3). Unless specified otherwise, we report performance on the validation splits. All reported datasets use accuracy as their metric.

Citation

@misc{sanh2021tzero,
  title={Multitask Prompted Training Enables Zero-Shot Task Generalization},
  author={Victor Sanh et al. (2021)},
  year={2021},
  note={arXiv:2110.08207}
}

• arXiv: 2110.08207