Advanced Topics and Future Directions
Goals: Equip students with an understanding of the latest trends in LLM.
This section examines the nuances of multimodal LLMs, scaling laws in training, sophisticated prompting frameworks like ReAct, and the complexities of training on generated data. The participants will unravel the ways they redefine interactions with language models. FlashAttention, Sparse Attention, and ALiBi's roles in expanding the context window offer insights into LLM optimization strategies. The module also confronts the challenges of training on generated data, spotlighting the critical phenomenon of Model Collapse.
- Multimodal LLMs: Discover the significance of multimodality in LLMs, integration of varied data like text and images. The lesson highlights popular models adept at multimodality and provides an overview of their functionalities.
- Scaling Laws in LLM Training: To achieve compute-optimal training results, it's essential to maintain a harmonious balance between the size of the LLM and the number of training tokens. This session offers guidance on how to effectively scale both elements in tandem.
- ReAct framework and ChatGPT plugins: The ReAct framework offers a sophisticated approach to enhancing interactions with language models, which also abilitates ChatGPT plugins.
- Expanding the context window: Detailed workings of FlashAttention and Sparse Attention in the transformer structures, aiming for efficient computation. Emphasis is also given to ALiBi's significance in this context.
- Training on generated data: Model Collapse: The problem of training on generated data, particularly emphasizing the phenomenon of Model Collapse. Understanding this topic is crucial, as training on bad data influences the effectiveness and reliability of LLMs.
- New Challenges in LLM Research: The Emerging challenges in LLM research, are cover agents, retriever architectures, larger context windows, efficient attention, and cost-effective pre-training and fine-tuning. Insights from recent studies guide the exploration, preparing students for the evolving LLM landscape.
As we wrap up this comprehensive course on Training and Fine-Tuning Large Language Models, students have covered a vast spectrum of topics, from the basic architecture of LLMs to deployment, from specialized Fine-Tuning methods to anticipated challenges in LLM research. With this knowledge, students are well-prepared to apply LLMs in practical scenarios, evaluate their performance effectively, innovate with fine-tuning strategies, and stay abreast of emerging trends and challenges in the domain of artificial intelligence and machine learning.