tl;dr: This lecture delves into instruction tuning, a strategic approach to enhancing the performance of LLMs in following detailed and complex instructions, discussing methods and best practices for collecting appropriate training data, modifying loss functions, and understanding the resulting model behaviours.

🎓 Lecturer: Gaurav Pandey [  / gaurav-pandey-11321120  ]
🔗 Get the Slides Here: http://lcs2.in/llm2401
📚 Suggested Readings:
[Instruction Tuning for Large Language Models: A Survey](https://arxiv.org/pdf/2308.10792)
[The Flan Collection: Designing Data and Methods for Effective Instruction Tuning](https://arxiv.org/pdf/2301.13688)
[SELF-INSTRUCT: Aligning Language Models with Self-Generated Instructions](https://aclanthology.org/2023.acl-lon...)
[WizardLM: Empowering Large Language Models to Follow Complex Instructions](https://arxiv.org/pdf/2304.12244)
[Orca: Progressive Learning from Complex Explanation Traces of GPT-4](https://arxiv.org/pdf/2306.02707)

This lecture focuses on the process of instruction tuning for Large Language Models (LLMs), a technique that enhances their ability to understand and execute specific instructions more effectively. We will cover the essential aspects of data collection, loss function adjustments, and the properties of models tuned with instructional data. This session is crucial for anyone interested in making LLMs more responsive and adaptable to complex user demands.