Balancing Efficiency and Privacy in Scaling Large Language Models for Math Problem Solving
Introduction to Large Language Models in Mathematics Large language models (LLMs) have shown remarkable ability in tackling complex mathematical problems. These models generate solutions by interpreting and predicting sequences of symbols and expressions. However, deploying such models effectively and efficiently at scale requires more than just having a powerful checkpoint. It involves a delicate balance between computational efficiency and preserving data privacy during inference. Challenges in Achieving Efficient Inference Efficient inference for LLMs solving math problems is hindered by multiple factors. First, the serving infrastructure must handle large computational loads without excessive latency. Second, quantization methods that reduce model size and speed up computation can introduce precision loss. Third, decoding strategies that generate output sequences can vary in speed and accuracy. Combining these elements often involves disparate tools that lack seamless in...