Label wording matters significantly for ranking models: augmentation helps with natural language but provides no benefit for abbreviations or codes, suggesting different strategies are needed for different label types.
This paper evaluates how changing label wording affects a multilingual AI model's ability to rank options. Testing a Russian cross-encoder on routing labels, the authors find that augmenting labels with multiple surface forms helps with ordinary words and jargon, but fails on abbreviations and opaque codes.