SimCSE achieves 76.3% unsupervised and 81.6% supervised Spearman's correlation on STS tasks with BERT-base, improving prior best results by 4.2% and 2.2% via simple contrastive learning.
A Continuously Growing Dataset of Sentential Paraphrases
4 Pith papers cite this work. Polarity classification is still indexing.
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cs.CL 4representative citing papers
HTEB introduces dynamic, multi-axis evaluation of text embedding robustness using LLM transformations, finding decoupled profiles across models and that scaling does not close all robustness gaps.
LAG-XAI treats paraphrasing as affine flows in semantic manifolds using Lie-inspired approximations, achieving AUC 0.7713 on paraphrase detection and 95.3% hallucination detection on HaluEval.
Data-CUBE applies a two-level curriculum (TSP-based task ordering via simulated annealing plus difficulty-sorted mini-batches) to multi-task instruction tuning and reports gains on MTEB sentence representation tasks.
citing papers explorer
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SimCSE: Simple Contrastive Learning of Sentence Embeddings
SimCSE achieves 76.3% unsupervised and 81.6% supervised Spearman's correlation on STS tasks with BERT-base, improving prior best results by 4.2% and 2.2% via simple contrastive learning.
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The Harder Text Embedding Benchmark (HTEB): Beyond One-dimensional Static Robustness
HTEB introduces dynamic, multi-axis evaluation of text embedding robustness using LLM transformations, finding decoupled profiles across models and that scaling does not close all robustness gaps.
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LAG-XAI: A Lie-Inspired Affine Geometric Framework for Interpretable Paraphrasing in Transformer Latent Spaces
LAG-XAI treats paraphrasing as affine flows in semantic manifolds using Lie-inspired approximations, achieving AUC 0.7713 on paraphrase detection and 95.3% hallucination detection on HaluEval.
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Data-CUBE: Data Curriculum for Instruction-based Sentence Representation Learning
Data-CUBE applies a two-level curriculum (TSP-based task ordering via simulated annealing plus difficulty-sorted mini-batches) to multi-task instruction tuning and reports gains on MTEB sentence representation tasks.