A Survey of Metrics to Enhance Training Dependability in Large Language Models

The rapidly advancing field of artificial intelligence requires meticulous attention to the training and monitoring of large language models (LLMs). This paper offers a systematic analysis of existing metrics and introduces new ones, focusing on their theoretical underpinnings and practical implementations. We present empirical results and insights into the performance of selected metrics, elucidating the complex interplay of variables in the training process. Our comprehensive approach provides significant insights into LLM training, and promises to improve the dependability and efficiency of future models.