Summary of Revealing Trends in Datasets From the 2022 Acl and Emnlp Conferences, by Jesse Atuhurra et al.
Revealing Trends in Datasets from the 2022 ACL and EMNLP Conferences
by Jesse Atuhurra, Hidetaka Kamigaito
First submitted to arxiv on: 31 Mar 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: 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 The Transformer architecture has revolutionized natural language processing (NLP), giving birth to pre-trained large language models (PLMs). As a result, NLP systems have achieved impressive performance gains across various tasks, often outperforming humans. However, a crucial factor in PLM performance is the quality of datasets used during pretraining. This paper aims to identify trends and insights within these datasets, providing valuable suggestions for future dataset curation. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary NLP has made huge progress thanks to the Transformer model. Big language models are getting better and better at tasks like text analysis. Even humans can’t beat them sometimes! The key is having good data to train on. That’s why researchers keep creating new datasets to help with specific problems. This study looks at what they’ve learned from these datasets and offers tips for making even better ones. |
Keywords
» Artificial intelligence » Natural language processing » Nlp » Pretraining » Transformer
