Summary of Encoder Vs Decoder: Comparative Analysis Of Encoder and Decoder Language Models on Multilingual Nlu Tasks, by Dan Saattrup Nielsen et al.
Encoder vs Decoder: Comparative Analysis of Encoder and Decoder Language Models on Multilingual NLU Tasks
by Dan Saattrup Nielsen, Kenneth Enevoldsen, Peter Schneider-Kamp
First submitted to arxiv on: 19 Jun 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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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 This paper investigates the performance of encoder and decoder language models on multilingual Natural Language Understanding (NLU) tasks, focusing on Germanic languages. The authors extend the ScandEval benchmark to evaluate decoder models, introducing a new method for evaluating NLU tasks. Experiments are conducted on Danish, Swedish, Norwegian, Icelandic, Faroese, German, Dutch, and English languages. Results show that encoder models outperform decoder models despite having fewer parameters, while UMAP analysis reveals unique capabilities of each model type. This study contributes to understanding language model paradigms in NLU tasks and provides insights for model selection and evaluation. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research looks at how well computer programs can understand many languages, including Germanic ones like Danish, Swedish, and English. The scientists made a tool to test these programs’ abilities and found that one type of program (encoder) is better than another (decoder) even though the decoder has more parts. They also looked at what kind of tasks these programs are good for and how they compare across different languages. |
Keywords
» Artificial intelligence » Decoder » Encoder » Language model » Language understanding » Umap
