Interpretable Text Embeddings and Text Similarity Explanation: A Survey
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866916983763632128 |
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| author | Opitz, Juri Möller, Lucas Michail, Andrianos Padó, Sebastian Clematide, Simon |
| author_facet | Opitz, Juri Möller, Lucas Michail, Andrianos Padó, Sebastian Clematide, Simon |
| contents | Text embeddings are a fundamental component in many NLP tasks, including classification, regression, clustering, and semantic search. However, despite their ubiquitous application, challenges persist in interpreting embeddings and explaining similarities between them. In this work, we provide a structured overview of methods specializing in inherently interpretable text embeddings and text similarity explanation, an underexplored research area. We characterize the main ideas, approaches, and trade-offs. We compare means of evaluation, discuss overarching lessons learned and finally identify opportunities and open challenges for future research. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_14862 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Interpretable Text Embeddings and Text Similarity Explanation: A Survey Opitz, Juri Möller, Lucas Michail, Andrianos Padó, Sebastian Clematide, Simon Computation and Language Artificial Intelligence Information Retrieval Text embeddings are a fundamental component in many NLP tasks, including classification, regression, clustering, and semantic search. However, despite their ubiquitous application, challenges persist in interpreting embeddings and explaining similarities between them. In this work, we provide a structured overview of methods specializing in inherently interpretable text embeddings and text similarity explanation, an underexplored research area. We characterize the main ideas, approaches, and trade-offs. We compare means of evaluation, discuss overarching lessons learned and finally identify opportunities and open challenges for future research. |
| title | Interpretable Text Embeddings and Text Similarity Explanation: A Survey |
| topic | Computation and Language Artificial Intelligence Information Retrieval |
| url | https://arxiv.org/abs/2502.14862 |