Hey everyone, We’re now stepping into the second half of our course, focusing much more on evaluating LLMs and their real-world impacts. We’re moving into territory where there aren’t always clear answers, and I’ll increasingly count on your interests and contributions to help steer our discussions, less lecturing, more chatting and debating. For Monday: We’re […]
Continue reading →Some aspects of Andrew Abbott’s critique resonate with me. For instance, he points out that some students only read the highlighted portions of the text, bypassing the development of a logical argument that comes with reading the entire passage. Having attended his class for two weeks, I confess to being one of those students who […]
Continue reading →Dr. Chomsky’s argument against LLMs primarily focuses on how despite the perceived intelligence of a model, any given is unable to process information the way humans can, which, in his opinion, poses a limit on LLMs. I agree that the way LLMs are constructed limit their ability to imitate humans, however I think Chomsky is […]
Continue reading →Chomsky’s counterargument, in my view, lacks clarity. He contends that “The essence of machine learning is about description and prediction; it doesn’t delve into causal mechanisms or physical laws.” Yet, when we prompt advanced models like GPT 3.5 or GPT 4 with questions such as why apples fall, they can provide explanations that touch on […]
Continue reading →From Week 4, we’ll start our Monday class for an about 40 minutes lecture finishing the two approaches to language models, and after that I’ll talk about why and how to make language models large. Then we’ll spend the second half of Monday class and the whole Wednesday class to deepen our exploration into the behaviors, […]
Continue reading →Guidelines and Suggestions: 1. Scope of Ideas: While the suggestions provided are meant to kickstart your brainstorming process, they’re not exhaustive. Embrace your creativity and propose unique ideas. (You’re welcome to be more innovative and bold than I am!) However, consider the project’s timeframe – about four weeks. 2. Project Milestones: Even if by […]
Continue reading →To read Ouyang (2022)’s paper, I tried ChatGPT’s PDF plugin to assist reading the paper, and it seems to work well. In this response I will bring up two points, one for my own reaction to the paper and another on the attempt to use ChatGPT assistance. I find the notion of “alignment” very interesting […]
Continue reading →These readings inform me of the history of the development of cognitive science, in particular, how certain concepts that I take for granted (mechanism, machine, computation, information, symbol, etc.) came onto the main stage. There are two main thoughts I had as I read through this history: I noticed a distinctive approach to the Mind-Body […]
Continue reading →Syllabus for Perspectives on Large Language Models: Computational, Cognitive, Social at The University of Chicago (Cognitive Science 20100, Linguistics 20110, Fall 2023) Instructor: Eugene Yu Ji (yuji1@uchicago.edu) Time and Location: Mon and Wed 3:00 – 4:20 pm Harper Mem Library 130 Office hours: Weds 8 – 9 pm Online or by appointment Description: In this […]
Continue reading →Welcome to the University of Chicago’s course site for “Perspectives on Large Language Models: Computational, Cognitive, Social” (Fall 2023). The course is an intellectual journey that invites around 20 undergraduate students at UChicago to critically analyze, discuss, and debate about the cutting-edge advancements in LLMs and AI, as well as their impacts on education, science, […]
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