Entry requirements

120 credits including Probability and Statistics, Linear Algebra, Single Variable Calculus and a course in introductory programming. Statistical Machine Learning. Proficiency in English equivalent to the Swedish upper secondary course English 6.

Learning outcomes

On completion of the course, the student should be able to:

• Explain the fundamentals about large language models, their capabilities and limitations.
• Explain important principles behind how large language models work, the model architectures and training algorithms.
• Document data usage and describe the role of data in determining the model performance.
• Build a large language model based chatbot application, explain its use and associated ethical considerations.
• Describe and analyse the social biases and harms associated with large language models.
• Critically examine the potential societal consequences of artificial intelligence for ethics and sustainability.

Content

This course gives an overview of large language models, the possibilities and harms associated with it, and the societal consequences of artificial intelligence. The focus is on the fundamentals of large language models: model architectures, types of language models, training algorithms, data usage; key concepts of tokenization, embeddings, attention mechanism, transformers, prompting, fine-tuning; building a large language model based chatbot application; ethical considerations; and case-studies on: social biases and stereotypes, toxicity, misinformation, use and misuse within education, and the environmental impact.

Instruction

Lectures, seminars, computer laboratory work, assignments and group projects.

Assessment

Assignments, shorter exercises in computer laboratories, and group projects with oral presentation and a written report.

If there are special reasons for doing so, an examiner may make an exception from the method of assessment indicated and allow a student to be assessed by another method. An example of special reasons might…