Explore the future of large language models in this seminar by Luke Zettlemoyer at MIT. Delve into the challenges and possibilities of scaling language models, including sparse mixtures of experts (MoEs) models with reduced cross-node communication costs. Learn about innovative prompting techniques that control for surface form variation, improving performance without extensive task-specific fine-tuning. Discover new forms of supervision for language model training, such as learning from hypertext and multi-modal web page structures. Gain insights into the potential next generation of NLP models, covering topics like modern NLP scaling, algorithmic optimization, parallel training, domain structure, and inference procedures. Examine the benefits and challenges of modular approaches, perplexity numbers, and the fundamental challenges of generic language models. Investigate the role of noisy channel models, fine-tuning, and scoring strings in improving model performance. Consider the impact of web crawls, structured data efficiency, and multimodality on the future of language models. Read more