2 citations · 2 across the 2 of their papers we have counts for
3 papers
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…
How to Measure the Intelligence of Large Language Models?
Nils Körber, Silvan Wehrli, Christopher Irrgang
With the release of ChatGPT and other large language models (LLMs) the discussion about the intelligence, possibilities, and risks, of current and future models have seen large att…
German Text Embedding Clustering Benchmark
Silvan Wehrli, Bert Arnrich, Christopher Irrgang
This work introduces a benchmark assessing the performance of clustering German text embeddings in different domains. This benchmark is driven by the increasing use of clustering n…