Summary of Revisiting Dynamic Evaluation: Online Adaptation For Large Language Models, by Amal Rannen-triki et al.
Revisiting Dynamic Evaluation: Online Adaptation for Large Language Models
by Amal Rannen-Triki, Jorg Bornschein, Razvan Pascanu, Marcus Hutter, Andras György, Alexandre Galashov, Yee Whye Teh, Michalis K. Titsias
First submitted to arxiv on: 3 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 In this paper, researchers investigate the benefits of dynamically updating language models at test time, a process known as dynamic evaluation or online fine-tuning. Building upon existing work that highlights the advantages of this approach in handling distributional shifts between training and evaluation data, the authors emphasize its connection to concepts like memory in neuroscience. They focus on three key aspects: speed of adaptation, sensitivity to distributional drift, and computational overhead. The study provides insights into when online adaptation is particularly useful and blurs the distinction between in-context learning and fine-tuning by conditioning models on previously observed tokens. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Language models can adapt at test time! This means they change their behavior based on new information. Researchers looked into this “online fine-tuning” to see how it helps language models work better, especially when there’s a big difference between the data used to train and test the model. They also explored what happens when the model is updated quickly or slowly, and how much extra computation is needed. The study found that online adaptation can be very useful in certain situations and made the connection between this process and how our brains work. |
Keywords
* Artificial intelligence * Fine tuning
