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Mechanistic Decomposition of Sentence Representations
Matthieu Tehenan, Vikram Natarajan, Jonathan Michala +2
Sentence embeddings are central to modern NLP and AI systems, yet little is known about their internal structure. While we can compare these embeddings using measures such as cosin…
Adapting Multilingual Embedding Models to Historical Luxembourgish
Andrianos Michail, Corina Julia Raclé, Juri Opitz +1
The growing volume of digitized historical texts requires effective semantic search using text embeddings. However, pre-trained multilingual models face challenges with historical…
Interpretable Text Embeddings and Text Similarity Explanation: A Survey
Juri Opitz, Lucas Möller, Andrianos Michail +2
Text embeddings are a fundamental component in many NLP tasks, including classification, regression, clustering, and semantic search. However, despite their ubiquitous application,…
Sentence Smith: Controllable Edits for Evaluating Text Embeddings
Hongji Li, Andrianos Michail, Reto Gubelmann +2
Controllable and transparent text generation has been a long-standing goal in NLP. Almost as long-standing is a general idea for addressing this challenge: Parsing text to a symbol…