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This paper audits GUI grounding evaluations, revealing that high instruction-element embedding similarity often reflects visible-label recovery rather than semantic grounding, and advocates for reporting lexical baselines and diagnostics.
MyAwesomeModel, a BERT-based feature extractor, converts sentences into dense vectors for semantic search, clustering, and NLP pipelines.
This paper evaluates whether multilingual sentence embeddings can replace translation for linguistic-integrated reliability auditing across multiple languages in educational assessments, finding that native-language embeddings reproduce translation-based reliability estimates closely.
Proposes using Top-k Sparse Autoencoders to disentangle dense sentence embeddings into human-interpretable concepts, enabling steering of retrieval results without retraining.
This paper investigates whether sentence embeddings can serve as an inference-time interface for injecting geological knowledge into a learned Darcy-flow inverse solver, finding that text conditioning reduces reconstruction error by 81% relative to a no-text counterfactual, with most gains from categorical class-level constraints.
This paper develops a geometric framework to measure semantic content of texts using sentence embeddings, proposing a three-coordinate semantic profile (novelty, breadth, integration) and a scalar trade-off triangle, validated across synthetic categories and novels.
This paper presents embeddingmagibu-200m, a Turkish-focused sentence embedding model built via cross-lingual tokenizer surgery and offline distillation, achieving strong performance on Turkish benchmarks with a cost-quality trade-off.
This model maps sentences to 384-dimensional vectors for tasks like clustering and semantic search, fine-tuned on 1B sentence pairs using contrastive learning. It is part of the sentence-transformers library and can be used with Hugging Face Transformers.