Summary of The Scandinavian Embedding Benchmarks: Comprehensive Assessment Of Multilingual and Monolingual Text Embedding, by Kenneth Enevoldsen et al.
The Scandinavian Embedding Benchmarks: Comprehensive Assessment of Multilingual and Monolingual Text Embedding
by Kenneth Enevoldsen, Márton Kardos, Niklas Muennighoff, Kristoffer Laigaard Nielbo
First submitted to arxiv on: 4 Jun 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed Scandinavian Embedding Benchmark (SEB) aims to address the lack of benchmarks for evaluating multilingual text embeddings in Scandinavian languages. To achieve this, SEB is designed as a comprehensive framework that enables evaluation across 24 tasks, 10 subtasks, and 4 task categories. The authors evaluate over 26 models using SEB, revealing significant performance disparities between public and commercial solutions, which were not captured by the existing MTEB benchmark. By integrating SEB with MTEB, the study bridges the text embedding evaluation gap for Scandinavian languages. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper introduces a new way to test how well computer programs can understand Scandinavian languages like Swedish and Norwegian. Right now, there’s no good way to compare different language-understanding models in these languages. The researchers created a special testing tool called SEB (Scandinavian Embedding Benchmark) that lets them see how 26 different language-understanding models perform on many different tasks. |
Keywords
» Artificial intelligence » Embedding » Language understanding
