Summary of Ensembling Finetuned Language Models For Text Classification, by Sebastian Pineda Arango et al.
Ensembling Finetuned Language Models for Text Classification
by Sebastian Pineda Arango, Maciej Janowski, Lennart Purucker, Arber Zela, Frank Hutter, Josif Grabocka
First submitted to arxiv on: 25 Oct 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 This paper investigates the effectiveness of ensembling finetuned models for text classification, a common practice in various communities. The authors present a metadataset featuring predictions from five large finetuned models on six datasets, and evaluate different ensembling strategies to improve performance and provide reliable uncertainty estimates. By leveraging these findings, practitioners can optimize their approaches to achieve better results in text classification tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is all about using many trained AI models together to make better decisions when classifying texts into categories. Right now, people often use just one trained model for this task, but the authors want to see if combining multiple models can do even better. They created a big dataset with predictions from five top-performing AI models on six different text datasets and tested various ways of combining these predictions. Their results show that combining these models can indeed improve performance and help predict which texts belong in what categories. |
Keywords
» Artificial intelligence » Text classification
