Language Modeling Loss

Language modeling loss functions are crucial for training large language models (LLMs), guiding them to generate coherent and contextually relevant text. Current research focuses on improving these loss functions to address issues like the underutilization of preference data in recommendation systems, the mitigation of translationese in machine translation, and the efficient scaling of model training. This involves exploring novel loss functions, such as those incorporating negative samples, language-driven objectives in embedding spaces, and meta-learned loss scaling for online adaptation, ultimately aiming for more accurate, efficient, and robust LLMs. These advancements have significant implications for various NLP tasks, including machine translation, question answering, and text generation, leading to improved model performance and reduced computational costs.