> For the complete documentation index, see [llms.txt](https://www.mlcompendium.com/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://www.mlcompendium.com/natural-language-processing-nlp/a-reality-check.md).

# A Reality Check

Before reading any NLP leaderboard, it is worth asking whether the gains survive a fair comparison. The metric-learning reality check says many do not: once datasets are normalized, many algorithms show little metric progress. The page gives the benchmark, the paper, and the follow-up note that make that stall visible.

The same notes are in [A metric learning reality check](/evals/evaluation-metrics.md#a-metric-learning-reality-check).

The tool behind the comparison is [A powerful benchmark](https://github.com/KevinMusgrave/powerful-benchmarker), a library for ML benchmarking. The argument itself is the [paper](https://arxiv.org/pdf/2003.08505.pdf), "A Metric Learning Reality Check", and the [medium](https://medium.com/@tkm45/updates-to-a-metric-learning-reality-check-730b6914dfe7) post carries the updates to it — together they show that normalizing data sets allows us to see that there wasn't any advancement in terms of metrics in many NLP algorithms.

## Deprecated links

{% hint style="warning" %}
These links and images no longer work. The original wording is kept here. A same-resource copy, when one was checked, is used above.
{% endhint %}

* A powerful benchmark This address no longer opens: <https://github.com/KevinMusgrave/powerful-benchmarke>


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