Numerical Error Analysis of Large Language Models
This work addresses computational instabilities in LLMs for NLP practitioners, offering incremental improvements in error mitigation.
The paper tackles the problem of numerical errors in large language models by analyzing round-off errors in transformer forward passes, providing theoretical bounds and practical guidelines to improve robustness and stability.
Large language models based on transformer architectures have become integral to state-of-the-art natural language processing applications. However, their training remains computationally expensive and exhibits instabilities, some of which are expected to be caused by finite-precision computations. We provide a theoretical analysis of the impact of round-off errors within the forward pass of a transformer architecture which yields fundamental bounds for these effects. In addition, we conduct a series of numerical experiments which demonstrate the practical relevance of our bounds. Our results yield concrete guidelines for choosing hyperparameters that mitigate round-off errors, leading to more robust and stable inference.