SASA replaces single-vector decoders in SAEs with learned subspaces plus block sparsity and nuclear-norm regularization, proving that a single group becomes the global minimizer once block size meets intrinsic dimension and yielding polynomial rather than exponential sample complexity.
Word10(2-3), 146–162 (Aug 1954)
6 Pith papers cite this work. Polarity classification is still indexing.
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Semantic mapping of 8,954 definitions and 2,700 scales from 14,000+ papers shows learner agency and autonomy span task regulation, personal motivation, and sociocultural dimensions, with existing scales and generative AI research underrepresenting the sociocultural dimension.
The paper guides ML use in economic history, identifies systematic prediction bias that distorts coefficients, and shows debiasing via small expert-labeled samples can correct it while preserving scale.
New Zealand Reddit users link language to place and form contiguous speech communities with complex geographic alignment; Word2Vec embeddings reveal semantic variations and shifts in NZ English on a 4.26 billion word corpus.
Open-weight instruction-aware encoders capture equal or greater affective information than proprietary models at word level across emotion theories, while task-tuned and proprietary encoders perform best on sentence-level classification.
Gyan is a novel explainable non-transformer language model that achieves SOTA results on multiple datasets by mimicking human-like compositional context and world models.
citing papers explorer
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Language, Place, and Social Media: Geographic Dialect Alignment in New Zealand
New Zealand Reddit users link language to place and form contiguous speech communities with complex geographic alignment; Word2Vec embeddings reveal semantic variations and shifts in NZ English on a 4.26 billion word corpus.
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A Comparative Study on Affective Cues in Text Embeddings Across Psychological Emotion Theories
Open-weight instruction-aware encoders capture equal or greater affective information than proprietary models at word level across emotion theories, while task-tuned and proprietary encoders perform best on sentence-level classification.
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Gyan: An Explainable Neuro-Symbolic Language Model
Gyan is a novel explainable non-transformer language model that achieves SOTA results on multiple datasets by mimicking human-like compositional context and world models.