Summary of Less Is More: Parameter-efficient Selection Of Intermediate Tasks For Transfer Learning, by David Schulte et al.
Less is More: Parameter-Efficient Selection of Intermediate Tasks for Transfer Learning
by David Schulte, Felix Hamborg, Alan Akbik
First submitted to arxiv on: 19 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 A new approach to task transfer learning in NLP is proposed, which can significantly improve model performance on emotion detection and other tasks. The authors introduce Embedding Space Maps (ESMs), lightweight neural networks that approximate the effect of fine-tuning a language model. ESMs enable efficient evaluation of task rankings for large source pools, reducing execution time by a factor of 10 and disk space usage by a factor of 278 while retaining high selection performance. The study conducts the largest experiment on NLP task transferability and task selection with 12k source-target pairs, demonstrating the effectiveness of ESMs in improving model performance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us learn how to improve machine learning models by sharing knowledge from one task to another. For example, if we have little data for recognizing emotions, we can use a pre-trained language model and fine-tune it on a sentiment classification dataset. But which task should we choose? The authors developed a new way to quickly evaluate different tasks without having to run the entire process again. This new method is called Embedding Space Maps (ESMs). They tested this approach with many different source and target tasks, showing that it can greatly improve model performance. |
Keywords
» Artificial intelligence » Classification » Embedding space » Fine tuning » Language model » Machine learning » Nlp » Transfer learning » Transferability
