THIS REPOSITORY WILL EVOLVE OVER THE DURATION OF THE COURSE. WE WILL ADD CONTENT AS WE GO.
Time: Fall 2024 / Period 2
Target group: Master's students
Teachers:
- Aarne Talman, Accenture, [email protected]
- Dmitry Kan, TomTom, [email protected]
- Jussi Karlgren, Silo AI, [email protected]
Prerequisites:
- Python coding experience
- Basics of machine Learning (e.g. Machine Learning for Linguists (LDA-T317, KIK-LG210))
This hands-on course delves into the world of Large Language Models (LLMs) and their applications in Natural Language Processing (NLP). Students will gain understanding of how LLMs work, how to fine-tune them for specific tasks, and how to leverage their capabilities for various NLP applications. Through weekly lectures and coding labs, students will gain practical experience working with state-of-the-art LLMs and explore their potential to revolutionize the field of NLP.
The course will be evaluated based on the submission of a final report.
Students will need to submit a final report that covers all the labs:
What was done in each lab? What was the motivation behind your solutions? What did you learn? Challenges you encountered?
| Week | Dates | Topic / Lecture | Format | Teacher |
|---|---|---|---|---|
| 1 | 29/31.10. | Introduction to Generative AI and Large Language Models (LLM) | 90 min lecture and 90 min lab | Aarne |
| 2 | 05/07.11. | Using LLMs and Prompting-based approaches | 90 min lecture and 90 min coding lab | Aarne |
| 3 | 12/14.11. | Evaluating LLMs | 90 min lecture and 90 min coding lab | Jussi |
| 4 | 19/21.11. | Fine-tuning LLMs | 90 min lecture and 90 min coding lab | Aarne |
| 5 | 26/28.11. | Retrieval Augmented Generation (RAG) | 90 min lecture and 90 min coding lab | Dmitry |
| 6 | 03/05.12. | Use cases and applications of LLMs | 90 min lecture and 90 min coding lab | Dmitry |
| 7 | 10/12.12. | Final report preparation | Student work on their final report | Aarne |
Week 1: Introduction to Generative AI and Large Language Models (LLM)
- Overview of Generative AI and its applications in NLP
- Introduction to Large Language Models (LLMs) and their architecture
- Lab: Learn about tokenizers
Week 2: Using LLMs and Prompting-based approaches
- Understanding prompt engineering and its importance in working with LLMs
- Exploring different prompting techniques for various NLP tasks
- Hands-on lab: Experimenting with different prompts and evaluating their effectiveness
Week 3: Evaluating LLMs
- Understanding the challenges and metrics involved in evaluating LLMs
- Exploring different evaluation frameworks and benchmarks
- Hands-on lab: Evaluating LLMs using different metrics and benchmarks
Week 4: Fine-tuning LLMs
- Understanding the concept of fine-tuning and its benefits
- Exploring different fine-tuning techniques and strategies
- Hands-on lab: Fine-tuning an LLM for a specific NLP task
Week 5: Retrieval Augmented Generation (RAG)
- Understanding the concept of RAG and its advantages
- Exploring different RAG architectures and techniques
- Hands-on lab: Implementing a RAG system for a specific NLP task
Week 6: Use cases and applications of LLMs
- Exploring various real-world applications of LLMs in NLP
- Discussing the potential impact of LLMs on different industries
- Hands-on lab: query tables and generate synthetic data
Week 7: Final report preparation
- Students work on their final reports, showcasing their understanding of the labs and the concepts learned.
Group Project submission
- Final reports are submitted by 31st December 2024
Note: This syllabus is subject to change at the discretion of the instructors. Any modifications will be communicated to the students in a timely manner.