Quantifying Domain Knowledge in Large Language Models
Author:
Affiliation:
1. Kennesaw State University,Kennesaw,GA,USA
2. The Travelers Companies, Inc.,Hartford,CT,USA
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/10194967/10194965/10195056.pdf?arnumber=10195056
Reference5 articles.
1. Cross-lingual bert contextual embedding space mapping with isotropic and isometric conditions;xu,2021
2. Embedding Comparator: Visualizing Differences in Global Structure and Local Neighborhoods via Small Multiples
3. Does bert make any sense? interpretable word sense disambiguation with contextualized embeddings;wiedemann,2019
4. BERT: pre-training of deep bidirectional transformers for language understanding;devlin;Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics Human Language Technologies NAACL-HLT 2019 Minneapolis MN USA June 2-7 2019 Volume 1 (Long and Short Papers),2019
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1. Semanformer: Semantics-aware Embedding Dimensionality Reduction Using Transformer-Based Models;2024 IEEE 18th International Conference on Semantic Computing (ICSC);2024-02-05
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