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How Contextual are Contextualized Word Representations? Comparing the Geometry of BERT, ELMo, and GPT-2 Embeddings
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Replacing static word embeddings with contextualized word representations has yielded significant improvements on many NLP tasks.However, just how contextual are the contextualized representations produced by models such as ELMo and BERT?Are there infinitely many context-specific representations for each word, or are words essentially assigned one of a finite number of word-sense representations?For one, we find that the contextualized representations of all words are not isotropic in any layer of the contextualizing model.While representations of the same word in different contexts still have a greater cosine similarity than those of two different words, this self-similarity is much lower in upper layers.This suggests that upper layers of contextualizing models produce more context-specific representations, much like how upper layers of LSTMs produce more task-specific representations.In all layers of ELMo, BERT, and GPT-2, on average, less than 5% of the variance in a word's contextualized representations can be explained by a static embedding for that word, providing some justification for the success of contextualized representations.
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“Precedent exists for using PCA to extract network-level patterns from high-dimensional neural data, including fMRI (Smith et al., 2015), EEG (Blankertz et al., 2011), and even LLM embeddings (Ethayarajh, 2019).”
From Can "consciousness" be observed from large language model (LLM) internal states? Dissecting LLM representations obtained from Theory of Mind test with Integrated Information Theory and Span Representation analysis (2025) · cites this paper as Ethayarajh, 2019
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“Ethayarajh (2019) notes that unidirectional models produce embeddings that vary smoothly across tokens, implying that the difference operation can still capture meaningful transitions within a span.”
From Can "consciousness" be observed from large language model (LLM) internal states? Dissecting LLM representations obtained from Theory of Mind test with Integrated Information Theory and Span Representation analysis (2025) · cites this paper as Ethayarajh, 2019
Experimental — accuracy not yet measuredIn the citing paper
“Previous literature showed the BERT embeddings54 capture rich semantic information and are widely recognized as effective for sentence similarity tasks55–57.”
From Towards a holistic framework for multimodal LLM in 3D brain CT radiology report generation (2025) · cites this paper as [57]
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CLeBPI: Contrastive Learning for Bug Priority Inference
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Metadata
- Authors
- Ethayarajh, K.
- Affiliations
- Stanford University (US)
- Corresponding author
- Ethayarajh, Kawin
- Venue
- Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)
- Published
- 2019
- Pages
- 55-65
- OpenAlex
- W2971569798
- Cited by
- 841
- References
- 27
- Citations by year
- 2026: 93 · 2025: 92 · 2024: 115 · 2023: 160 · 2022: 158 · 2021: 157 · 2020: 64
- FWCI
- 36.26
- Type
- conference paper
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by
- Locations
- Conference on Empirical Methods in Natural Language Processing (EMNLP) (publishedVersion, cc-by)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- Natural Sciences and Engineering Research Council of Canada
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Physical Sciences › Computer Science › Artificial Intelligence › Natural Language Processing Techniques
- Physical Sciences › Computer Science › Artificial Intelligence › Explainable Artificial Intelligence (XAI)
- Keywords
- contextualized word representations, word embeddings, BERT, ELMo, GPT-2, embedding geometry, cosine similarity, static embeddings, layer-wise analysis
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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