LLM Working Group (February 2024)

February 26, 2024, 1:00pm to 2:30pm

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Location: Hybrid in-person in the D-Lab Collaboratory, 356 Social Sciences Building (3rd floor), or join us via Zoom! 

Date & Time: This hybrid event occurs on:

  • Monday, February 26 @ 1:00pm-2:30pm
    • Generative AI and the Digital Humanities: Tim Tanghlerlini, Greg Niemeyer, Lisa Wymore, and Kayla van Kooten will share experiences, posing questions about the future of LLMs in the context of the Digital Humanities.
  • Monday, March 18 @ 1:00pm-2:30pm
    • Teaching with LLMs: Kimberly Vinall, Emily Hellmich, Genevieve Smith, and Ben Spanbock will lead a dialogue on the potential of LLMs in reshaping educational landscapes.
  • Monday, April 22 @ 1:00pm-2:30pm
    • Understanding LLMs: Tarun Gogigeni, who is part of the Technical Staff at OpenAI, will discuss the state of the art of research on the inner workings and output of LLMs. Tarun works with John Schulman & Liam Fedus on RL and ChatGPT, and is a core contributor to GPT4 in the realm of Model Creativity.
  • Monday, May 13 @ 1:00pm-2:30pm
    • Researching with LLMs: Douglas Guilbeault and Chris Soria will delve into the use of LLMs as part of the researcher toolkit. We will discuss the use of APIs, prompt engineering, and other techniques to integrate LLMs into research.


The LLM Working Group is a community founded to facilitate conversations about Large Language Models (LLMs) and Generative AI within academia. This 4-part series will provide fundamental knowledge of LLMs, and generate conversation about the promises and challenges of LLMs in different facets of academic work.

In this first session, Generative AI and the Digital Humanities, Tim Tanghlerlini, Greg Niemeyer, Lisa Wymore, and Kayla van Kooten will share experiences, posing questions about the future of LLMs in the context of the Digital Humanities. We will discuss the future of DH research using LLMs, as well as the role of LLMs in producing creative work—literature, video, music, and so on—and the concomitant issues of ownership, creativity, and originality that come with this production.

Questions we will be addressing include:

  • Who has access to AI tools, and whose futures are determined by them?
  • Is AI fundamentally derivative, or does it mirror the human experience?
  • What is the role of the body in the future of AI?

LLM working Group sessions will be interactive, encouraging participants to share their experiences, pose questions, and collaboratively explore the challenges and potential of these technologies in their respective fields.

We encourage everyone to participate, regardless of their experience level with LLMs and GenAI. The LLM Working Group is a welcoming and supportive community for all.

This is a hybrid event. In-person seating is limited to 35.

About the Large Language Models (LLMs) Working Group

Dive into the Future of AI with the Large Language Models (LLMs) Working Group!

Celebrating ten years of innovation in data-intensive social science, D-Lab at UC Berkeley is excited to introduce the LLM Working Group, an initiative focused on the exploration and discussion of Large Language Models (LLMs) like ChatGPT within academic research and teaching.

This group aims to unite scholars, students, and data scientists to address crucial questions about AI's role in academia, including access, impact, creativity, and learning in the age of information automation. Through a series of interactive sessions, participants will gain insights into LLM capabilities, discuss ethical considerations, and explore innovative approaches to utilizing these tools in their work.

Whether you're an AI veteran or a novice curious about the potentials of Generative AI, the LLM Working Group offers a collaborative platform to learn, share, and shape the future of academic inquiry. Join us in navigating the world of LLMs together.

Location: Hybrid in-person in the D-Lab Collaboratory, 356 Social Sciences Building (3rd floor), or join us via Zoom!

Feedback: After completing the workshop, please provide us feedback using this form.

Questions? Email: dlab-frontdesk@berkeley.edu