Catwalk Default Metrics

---
name: catwalk-default-metrics
description: Evaluates language models across multiple dataset categories using standardized metrics attached to datasets rather than model implementations. Probes classification/entailment accuracy, question-answering fidelity, and language modeling fluency/probability calibration. Use when the user has predictions and gold and needs to compute Accuracy & relative improvement over random baseline, SQuAD metric.
metadata:
  skill_kind: metric
  source_arxiv: 2312.10253
  bibtex_key: [REDACTED]
  confidence: high
---

# catwalk-default-metrics

> Catwalk: A Unified Language Model Evaluation Framework for Many Datasets — Groeneveld et al. (2023) (arXiv:2312.10253, 2023)

## What this evaluates

Evaluates language models across multiple dataset categories using standardized metrics attached to datasets rather than model implementations. Probes classification/entailment accuracy, question-answering fidelity, and language modeling fluency/probability calibration.

## Datasets

- (no dataset; pure metric skill)

## Metrics

- `Accuracy & relative improvement over random baseline` **(primary)** — range: [0, 1] | percent
  - Computes standard accuracy for multiple-choice, classification, and entailment tasks. Also calculates relative improvement over a uniform random baseline.
- `SQuAD metric` **(primary)** — range: [0, 1]
  - Follows the exact implementation by Rajpurkar et al. (2016), computing token-level exact match and F1 score for open-ended question answering.
- `Perplexity per word, perplexity per byte, and entropy` — range: other
  - Computes average log-probability per token (perplexity per word), per byte of text, and Shannon entropy over the predicted token distribution for language modeling tasks.

## Input / output format

**Input**: Dataset-specific inputs: multiple-choice prompts, classification/entailment pairs, open-ended QA questions, or raw text sequences for language modeling.

**Output**: Model predictions including class labels, generated text spans, or token-level probability distributions.

## Scoring recipe

```python
def compute_metrics(predictions, gold, dataset_type, model):
    if dataset_type in ['multiple-choice', 'classification', 'entailment']:
        acc = mean(predictions == gold)
        random_acc = 1.0 / num_classes
        return {'accuracy': acc, 'relative_improvement': (acc - random_acc) / random_acc}
    elif dataset_type == 'qa':
        return squad_metric(predictions, gold) # Rajpurkar et al. 2016
    elif dataset_type == 'lm':
        log_probs = model.log_prob(predictions)
        return {
            'perplexity_per_word': exp(-mean(log_probs)),
            'perplexity_per_byte': exp(-mean(log_probs) / mean(len(token) for token in predictions)),
            'entropy': -mean(sum(p * log(p) for p in dist))
        }
```

## Common pitfalls

- Metrics are attached to datasets, not models; comparing models requires ensuring the same dataset-attached metrics are used.
- The Eleuther model style does not automatically compute suggested metrics, unlike other implemented styles.
- SQuAD metric refers to the specific Rajpurkar et al. (2016) implementation, which computes both exact match and F1, not just a single score.

## Evidence (verbatim from paper)

> By default, Catwalk computes the following metrics and can be easily extended to include others. - For multiple-choice, classification, and entailment datasets: Accuracy and relative improvement over the random baseline - For question-answering datasets: SQuAD metric as defined by Rajpurkar et al. (2016). - For language modeling datasets: Perplexity per word, perplexity per byte, and entropy.

## Citation

```bibtex
@misc{groeneveld2023catwalk,
  title={Catwalk: A Unified Language Model Evaluation Framework for Many Datasets},
  author={Groeneveld et al. (2023)},
  year={2023},
  note={arXiv:2312.10253}
}
```

- arXiv: 2312.10253