新闻
Label-Wording Augmentation in a Typed-Decision Encoder: Gains on Unfamiliar Words, Not on Abbreviations or Codes
arXiv cs.AI · 发布于 · 阅读约3分钟
30秒读懂
- 发生了什么
- Label-wording augmentation improved a Russian cross-encoder model's accuracy on unfamiliar words but provided no gains on abbreviations or opaque codes.
- 为何重要
- Matters when building routing or classification systems where you cannot control how users word their category labels at request time.
- 注意
- Gains on jargon and slang came with increased confident errors when choosing from many candidates; abbreviations saw no improvement or degraded performance.
- encoder
- eval
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