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arXiv AI By Vladimir Beskorovainyi

Label-Wording Augmentation in a Typed-Decision Encoder: Gains on Unfamiliar Words, Not on Abbreviations or Codes

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The paper evaluates a 307‑million‑parameter multilingual cross‑encoder, Ru‑Jev, on Russian‑language tasks, comparing its original and an augmented release that presents routing labels in multiple surface forms. Experiments show that the augmentation improves accuracy on unfamiliar ordinary words, borrowed jargon, and office slang—up to 0.24 points—while providing no benefit for abbreviations or opaque codes. The study also highlights evaluation hazards and notes that increased accuracy often coincides with reduced risk‑coverage, especially with many candidate labels.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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