Summary of Cmr Scaling Law: Predicting Critical Mixture Ratios For Continual Pre-training Of Language Models, by Jiawei Gu et al.
CMR Scaling Law: Predicting Critical Mixture Ratios for Continual Pre-training of Language Models
by Jiawei Gu, Zacc Yang, Chuanghao Ding, Rui Zhao, Fei Tan
First submitted to arxiv on: 24 Jul 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 The abstract proposes a novel approach to continual pre-training (CPT) of Large Language Models (LLMs) by discovering the power-law relationship between loss, mixture ratio, and training tokens scale. This allows for the formalization of the trade-off between general and domain-specific capabilities, leading to the concept of Critical Mixture Ratio (CMR). The CMR is shown to strike a balance between maintaining the model’s general ability and achieving the desired domain transfer, ensuring the highest utilization of available resources. The paper presents extensive experiments that substantiate the predictability of CMR and propose a scaling law for its application. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large Language Models (LLMs) are super smart computers that can do many tasks really well. But sometimes they don’t do as well in specific areas because they haven’t learned enough about those topics. To fix this, researchers have come up with a way to keep teaching the LLMs new things while still helping them remember what they already know. This is called continual pre-training (CPT). The problem is that people have been choosing how much of each type of training data to use based on rules rather than understanding how it really works. In this paper, researchers looked at how well LLMs do when they’re trained with different amounts of general and domain-specific data and found a special ratio that makes them work best. This means we can teach the computers in a way that balances what they already know with new information, making them more useful for specific tasks. |
