Concept Representations in Vector Space: Quantifying Sharedness and Idiosyncrasy of Meaning
cs.CL
Submitted: 2026-10-11
Updated: 2026-10-11
Sources
- MMTEB: Massive Multilingual Text Embedding Benchmark
- How Contextual are Contextualized Word Representations? Comparing the Geometry of BERT, ELMo, and GPT-2 Embeddings
- ColBERT: Efficient and Effective Passage Search via Contextualized Late Interaction over BERT
- RoBERTa: A Robustly Optimized BERT Pretraining Approach
- Efficient Estimation of Word Representations in Vector Space
- Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks
- Investigating the Frequency Distortion of Word Embeddings and Its Impact on Bias Metrics
- Attention Is All You Need
- Unsupervised Sentence Representation Learning with Frequency-induced Adversarial Tuning and Incomplete Sentence Filtering
- Language Models with Transformers
- MiniLM: Deep Self-Attention Distillation for Task-Agnostic Compression of Pre-Trained Transformers
- Revealing emergent human-like conceptual representations from language prediction
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