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About this paper
Deven M. Mistry, Ali A. Minai
Researchers at the University of Cincinnati developed an "in the wild" comparative study of eight sentence embedding models, assessing their consistency in inferring semantic variation within uncurated literary texts. The study found that while most models show high mutual agreement, architectural and training differences lead to distinct biases in semantic interpretation, particularly for InferSent.
Key insights
- Mutual consistency among different sentence embedding models can serve as an implicit cross-validation for assessing intrinsic semantic representations in the absence of explicit ground truth.
- Model architectural differences (e.g., RNN vs. Transformer) and training paradigms (e.g., supervised NLI vs. self-supervised) result in measurable biases and varying propensities to infer semantic similarity.
- Analyzing detailed agreement fractions (PAF, NAF, DDAF) provides a finer-grained understanding of model biases beyond simple correlation, revealing opposing interpretations of semantic relatedness.
Summary by alphaXiv.
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