LLMs-Exploratory-Research

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LLMs-Exploratory-Research

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D-Lab's 2-hour introduction to using LLMs for exploratory research.

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# D-Lab LLMs for Exploratory Research Workshop

[![License: CC BY 4.0](https://img.shields.io/badge/License-CC_BY_4.0-lightgrey.svg)](https://creativecommons.org/licenses/by/4.0/)

This repository contains the materials for D-Lab's LLMs for Exploratory Research workshop.

## Prerequisites

No prior experience in using LLMs is necessary for this workshop.

Check out D-Lab’s [Workshop Catalog](https://dlab-berkeley.github.io/dlab-workshops/) to browse all workshops, see what’s running now, and review prerequisites.

## Workshop Goals
In a fast evolving artificial intelligence landscape, LLMs such as GPT have become ubiquitous. In the research community, their advantages and pitfalls are hotly debated. In this workshop, we assess the different uses of LLMs in exploratory research and discusses ways to evaluate strengths and weaknesses of LLMs in various phases of exploratory research. First, we’ll cover some of the different types of LLMs that can be useful for research. Then, we’ll explore the use cases of LLMs specifically for exploratory research. As we go along, we will discuss the strengths and limitations of using LLMs for research.

## Learning Objectives

After this workshop, you will be able to:

- Use LLMs for tasks related to exploratory research.
- Compare and contrast outputs from different LLMs.

This workshop does not cover the following:

- How to use LLMs for coding.
- Introduction to GPT models. These are covered in [Python GPT Fundamentals](https://github.com/dlab-berkeley/Python-GPT-Fundamentals?search=1).

## Installation Instructions

We will be using the following LLM-chatbots during the workshop. You can create accounts for each by following the links:

1. [ChatGPT](https://chatgpt.com/)
2. [Perplexity](https://www.perplexity.ai/)
3. [Google Gemini](https://gemini.google.com/)
4. [Claude.ai](https://claude.ai/new)

You can view the workshop materials on GitHub and download the paper from the repository.

# About the UC Berkeley D-Lab

D-Lab works with Berkeley faculty, research staff, and students to advance data-intensive social science and humanities research. Our goal at D-Lab is to provide practical training, staff support, resources, and space to enable you to use R for your own research applications. Our services cater to all skill levels and no programming, statistical, or computer science backgrounds are necessary. We offer these services in the form of workshops, one-to-one consulting, and working groups that cover a variety of research topics, digital tools, and programming languages.

Visit the [D-Lab homepage](https://dlab.berkeley.edu/) to learn more about us. You can view our [calendar](https://dlab.berkeley.edu/events/calendar) for upcoming events, learn about how to utilize our [consulting](https://dlab.berkeley.edu/consulting) and [data](https://dlab.berkeley.edu/data) services, and check out upcoming [workshops](https://dlab.berkeley.edu/events/workshops).

# Other D-Lab LLM/GPT Workshops

Here are other LLM/GPT workshops offered by the D-Lab:

* [Python GPT Fundamentals](https://github.com/dlab-berkeley/Python-GPT-Fundamentals?search=1)

# Contributors

* [Kamya Yadav](https://dlab.berkeley.edu/people/kamya-yadav)
* Tom van Nuenen
* Pratik Sachdeva

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