Summary of Fine-tuned Network Relies on Generic Representation to Solve Unseen Cognitive Task, by Dongyan Lin
Fine-tuned network relies on generic representation to solve unseen cognitive task
by Dongyan Lin
First submitted to arxiv on: 27 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 abstract presents a study that investigates whether fine-tuned language models rely on their generic pretrained representations or develop new, task-specific solutions when encountering novel tasks. The researchers fine-tuned GPT-2 on a context-dependent decision-making task adapted from neuroscience literature and compared its performance to a model trained from scratch on the same task. The findings suggest that fine-tuned models heavily depend on their pretrained representations, particularly in later layers, while models trained from scratch develop different mechanisms. This highlights the advantages and limitations of pretraining for task generalization and emphasizes the need for further investigation into the underlying mechanisms. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The study explores how well language models can adapt to new tasks. Researchers took a popular model called GPT-2 and taught it to make decisions based on context. They compared this to teaching a brand new model from scratch to do the same task. The results show that the fine-tuned model uses its existing knowledge more than it develops new ways of thinking. This is important because it helps us understand how language models can be used in different situations. |
Keywords
» Artificial intelligence » Generalization » Gpt » Pretraining
