6 papers
Robustness of Misinformation Classification Systems to Adversarial Examples Through BeamAttack
Arnisa Fazla, Lucas Krauter, David Guzman Piedrahita +1
We extend BeamAttack, an adversarial attack algorithm designed to evaluate the robustness of text classification systems through word-level modifications guided by beam search. Our…
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…
MMTEB: Massive Multilingual Text Embedding Benchmark
Kenneth Enevoldsen, Isaac Chung, Imene Kerboua +83
Text embeddings are typically evaluated on a limited set of tasks, which are constrained by language, domain, and task diversity. To address these limitations and provide a more co…
Examining Multilingual Embedding Models Cross-Lingually Through LLM-Generated Adversarial Examples
Andrianos Michail, Simon Clematide, Rico Sennrich
The evaluation of cross-lingual semantic search models is often limited to existing datasets from tasks such as information retrieval and semantic textual similarity. We introduce…