About this Video

• Video Title: Gen-AI Workshop Session 3
• Channel: Financial Forensics
• Speakers: GenAI Workshop, HEMASURIYA K, Aditya singh, VIVEK BAJAJ, S R Srinivasan, Rakesh, Mahesh Balan U, LALIT KUMAR, POTIPIREDDY BHANU VENKATA AKHILESH, RAVINA, Prashant Negi, MOHAMMAD ABAN ALI, SANJAY B, BARATH CUDDALORE SRIDHAR, Mahi Mudgal, ANUJ JOKHANI, VIKRAM S, DEEPAK KUMAR, RAJAT GOEL, Saurabh Katti, Arvind S
• Duration: 03:22:10

Introduction

This workshop session covers the training of Large Language Models (LLMs), focusing on pre-training, supervised fine-tuning, and reinforcement learning techniques. The session builds upon previous sessions, reviewing key concepts before delving into more advanced topics like parameter-efficient fine-tuning and reinforcement learning with human feedback (RLHF). The instructor addresses participant questions and explores practical applications, including retrieval augmented generation (RAG).

Key Takeaways

1. LLM Training Stages: LLMs are trained in stages: pre-training (token prediction), supervised fine-tuning (improving accuracy on specific tasks), and reinforcement learning (aligning output with human preferences).
2. Parameter-Efficient Fine-Tuning: Techniques like LoRA (Low-Rank Adaptation) optimize fine-tuning by updating only a small subset of parameters, conserving resources and preventing overfitting with limited data.
3. Reinforcement Learning with Human Feedback (RLHF): RLHF improves model alignment with human preferences by using human feedback to rank model outputs, training a reward model to predict preferred responses, and then fine-tuning the LLM based on the reward model's scores.
4. Retrieval Augmented Generation (RAG): RAG addresses the limitations of LLMs' knowledge cutoffs by retrieving relevant information from external sources (e.g., databases, web search) to enhance responses. This reduces hallucinations and allows the model to access up-to-date information.
5. Choosing Between Fine-tuning and RAG: Fine-tuning is best for modifying the model's behavior or style; RAG is optimal for incorporating external knowledge sources, especially when the knowledge base is dynamic. Hybrid approaches combining both are also possible.

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However, I can offer significantly more detailed explanations of specific sections. To help me do this effectively, please choose one or two key topics from the video that you'd like me to expand upon. For example, you could ask for more details on:

• LLM Training Stages (Pre-training, Supervised Fine-tuning, Reinforcement Learning): I can explain each stage with more examples and clarify the differences between them.
• Parameter-Efficient Fine-tuning (PEFT): I can elaborate on techniques like LoRA and explain their advantages in detail.
• Reinforcement Learning with Human Feedback (RLHF): I can delve deeper into the process, explaining the role of human annotators and the reward model in more detail. I can also compare RLHF with pure reinforcement learning.
• Retrieval Augmented Generation (RAG): I can provide more examples of RAG applications, discuss different retrieval strategies (threshold vs. top-k), and explain how RAG addresses the limitations of LLMs.
• Fine-tuning vs. RAG: I can offer a more detailed comparison of when to use each approach and discuss hybrid strategies.
• The Gridworld Analogy and its Relation to LLMs: I can expand on how the Gridworld example illustrates reinforcement learning concepts and their parallels in LLM training.
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