Summary of To Cool or Not to Cool? Temperature Network Meets Large Foundation Models Via Dro, by Zi-hao Qiu et al.
To Cool or not to Cool? Temperature Network Meets Large Foundation Models via DRO
by Zi-Hao Qiu, Siqi Guo, Mao Xu, Tuo Zhao, Lijun Zhang, Tianbao Yang
First submitted to arxiv on: 6 Apr 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Optimization and Control (math.OC)
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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 research proposes a novel framework for learning a temperature prediction network (TempNet) to enhance the performance of large foundation models, such as large language models and CLIP models. The temperature parameter plays a crucial role in adjusting logits during next token generation and scaling similarities during contrastive loss training. The authors present a principled framework that combines constrained distributionally robust optimization and a properly designed TempNet. This approach allows for training TempNet from scratch or separately given a pre-trained foundation model, making it generalizable and transferable to new tasks. Experimental results on LLMs and CLIP models demonstrate significant performance improvements. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper explores how to improve large language models and other foundation models by learning a personalized temperature prediction network. The temperature setting is important for these models because it affects their behavior during training or use. Researchers developed a special framework that helps predict the best temperature for any given input data, which can make the model work better. This approach can be used to train new models from scratch or improve existing ones. Tests showed that this method works well and can even help with other tasks. |
Keywords
* Artificial intelligence * Contrastive loss * Logits * Optimization * Temperature * Token
