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About this paper
Hongliu Cao
Universal text embeddings, despite their broad capabilities, exhibit a significant "negation blindness," often misinterpreting negated statements as semantically similar to their original forms. A lightweight, parameter-free semantic adapter is introduced that significantly enhances negation awareness with minimal data and computational cost, improving accuracy by up to +15.86% on complex negation tasks while preserving the embeddings' overall universality.
Key insights
- Universal text embeddings show a substantial "negation blindness," consistently interpreting negated pairs as highly similar (e.g., in 99.27% of low-similarity cases for Group 1 in STSB).
- Negation information is implicitly encoded within the high-dimensional space of universal embeddings; a targeted re-weighting mechanism can amplify these inherent signals without full fine-tuning.
- The proposed adapter offers a highly sustainable and data-efficient approach, requiring minimal training data (200-500 samples) and computational resources (CPU-only, minutes of training), while maintaining the base model's universality.
Summary by alphaXiv.
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