Uncovering Scaling Laws for Large Language Models via Inverse Problems
This is an incremental position paper that addresses the problem of inefficient scaling in LLMs for researchers and practitioners.
The paper tackles the high cost of training large language models by proposing to use inverse problems to uncover scaling laws, aiming to guide model building for better performance with improved cost-effectiveness.
Large Language Models (LLMs) are large-scale pretrained models that have achieved remarkable success across diverse domains. These successes have been driven by unprecedented complexity and scale in both data and computations. However, due to the high costs of training such models, brute-force trial-and-error approaches to improve LLMs are not feasible. Inspired by the success of inverse problems in uncovering fundamental scientific laws, this position paper advocates that inverse problems can also efficiently uncover scaling laws that guide the building of LLMs to achieve the desirable performance with significantly better cost-effectiveness.