👉 Constantly Missed (CM) errors, often referred to as constantly missiles in a simplified context, are a common metric in machine learning and natural language processing that quantify the frequency with which a model fails to predict a target sequence correctly. In the context of language generation, this means the model frequently produces irrelevant or nonsensical text instead of the intended output. For example, if a language model is asked to write a story about a cat chasing a mouse but keeps generating descriptions of cats playing soccer, it's constantly missing the target. These errors highlight issues with the model's ability to maintain context, coherence, and relevance in its generated text, indicating a need for adjustments in training data, model architecture, or training parameters to improve its performance.
