CS291K - Special Topics in Large Language Models and Conversational AI home | schedule

Announcements

Feb 5: Both office hour and instruction will be online due to recent storm.  the zoom link is announced at canvas


Abstract: This is a graduate-level research course on Large Language Models and Conversational AI. Over the duration of this course, we will delve into the latest publications within the expansive domain of Large Language Models (LLMs) and Conversational AI. Specifically, our explorations will revolve around LLM-based techniques for dialogue systems, programming, intelligent agents, and multimodal learning. Each student is entrusted with the following responsibilities: Conducting critical analyses and authoring paper reviews, programming with the newest ChatGPT functions, delivering comprehensive paper presentations, and undertaking a substantial, research-quality course project. The intent behind this course is to foster a deep understanding of LLMs and Conversational AI and their pivotal role in today's technological landscape.

Each student is expected to read papers before lecture, write paper reviews or program with ChatGPT, present papers,  and complete a research-quality course project (e.g., implement an existing algorithm or solve a new problem creatively using deep learning. One team could have two students. ).  Projects that simply apply Transformers/ChatGPT are not encouraged.

Prerequisites: Neural network building experience or successfully finished an introductory deep learning course.

Instructor: Prof. Xifeng Yan

Time: Monday/Wednesday 5:00- 6:50pm, Location: PHELP 3526   Office Hour:  Monday 11:00-12:00pm, Henley Hall 2017

TA: N/A. 

Grading: Your grade will be derived from paper review or programming (15%),  paper presentation (15%), midterm quiz (15%), project presentation (15%), and project (40%)

Paper Reading: You are required to read every paper carefully before lecture.

Paper Review or Programming:  Option 1: Find two deep learning papers that are not presented in lecture but highly related to your project, and write a detailed 2-page review.  Your review shall include your understanding of the work, discuss its strength and weakness, and mostly important, propose new ideas to improve it.    A review will be graded by the quality of the paper (20%) and the quality of your review (80%).  Option 2: programming with systems like LangChain, Autogpt, Openai, etc.  A programming task will be graded by the quality of the task (40%) and the completion and report of your task (60%).

Text Books (not required)