Embedding Evaluation › Building an Evaluation Mindset
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Developing good evaluation habits is as important as understanding the methods themselves. A few principles guide effective embedding evaluation practice.
Always compare to baselines. A Spearman correlation of 0.65 on SimLex-999 is meaningless without context. Is that better than random vectors? Better than a simple word frequency baseline? Better than GloVe 50d trained on Wikipedia? Every evaluation number needs a reference point. Publish baseline numbers prominently, and always compare new models to established ones on the same benchmarks.
Report every result. It's tempting to select the evaluation metric where your model looks best and report only that. This is a form of evaluation cherry-picking that produces misleading science. Good practice is to commit to an evaluation protocol before seeing results and then report all metrics regardless of which model "wins."
Match evaluation to deployment context. If you're building a biomedical NLP system, evaluate on biomedical benchmarks (BioCreative, MedQA, clinical NER datasets), not just on general-domain benchmarks. General-domain evaluation scores are nearly useless for predicting performance on specialized text. The domain mismatch between training data, evaluation benchmarks, and deployment data is a persistent source of surprise performance drops in production.
Understand the human ceiling. Evaluation benchmarks have a natural upper bound set by human inter-annotator agreement. On SimLex-999, human agreement is about 0.67. A model scoring 0.75 is not obviously worse than a model scoring 0.78, since both are above the human agreement ceiling and the difference may be statistical noise. Understanding ceilings prevents you from drawing strong conclusions from small differences between good models.
Visualize before and after debiasing. WEAT provides numbers, but visualizing the embedding space before and after debiasing interventions often reveals whether the intervention worked or just moved the bias around. Project gendered word pairs before and after debiasing and check whether the geometric relationships changed as intended.