Summary of Towards Efficient Active Learning in Nlp Via Pretrained Representations, by Artem Vysogorets et al.
Towards Efficient Active Learning in NLP via Pretrained Representations
by Artem Vysogorets, Achintya Gopal
First submitted to arxiv on: 23 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computation and Language (cs.CL)
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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 In this paper, researchers propose a novel approach to fine-tuning large language models (LLMs) for text classification tasks, particularly when labeled documents are scarce. The strategy involves using pre-trained representations of LLMs within the active learning loop and then fine-tuning them on the acquired labeled data to achieve the best performance. This method yields similar results to fine-tuning all the way through the active learning loop but is significantly more computationally efficient. The approach also allows for flexibility in choosing the final model or updating it as newer versions are released. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us make computers better at understanding text by improving a technique called “active learning.” When we don’t have many labeled examples, this method can help us save time and money. The researchers found a way to use pre-trained language models within this process, which makes it much faster than before. Their approach is just as good as the original one but uses fewer computer resources. This means we can choose the best model for our task or update it with new information. |
Keywords
* Artificial intelligence * Active learning * Fine tuning * Text classification
