Introduction to Large Language Models (LLMs) in Social Sciences: Online (3 days)

Day 1: Foundations of Python and Introduction to LLMs

• Morning Session: Introduction to Python for Social Sciences
  • Overview of Python as a programming language
  • Introduction to Google Colab and basic Python syntax and concepts
  • Introduction to data preprocessing with Numpy and Pandas
• Case Demonstration:
  • Analysing a simple dataset (e.g., a CSV file containg survey responses) using pandas and drawing basic inferences
  • Key takeaway: Understanding how Python can be used to manipulate and analyse social science data

• Afternoon Session: Understanding Large Language Models (LLMs)
  • What are LLMs and how do they work?
  • Overview of the capabilities of LLMs
  • Ethics and considerations in using LLMs in Social Science Research
• Case Demonstration:
  • Using a pre-trained LLM to analyse text data (e.g., political speeches or social media posts) to extract themes and sentiments.
  • Key takeaway: intro to LLMs in qualitative data analysis.

Day 2: Hands-On with LLMs in Social Science Research

• Morning Session: Python Libraries for Working with LLMs
  • Introduction to Python libraries for LLMs (eg., transformers, OpenAI's GPT)
  • Simple text generation and text completion tasks using LLMs
• Case Demonstration:
  • Data augmentation in social sciences research: e.g. generating synthetic interview reponses based on a provided dataset.
  • Key takeaway: How LLMs can be used for data augmentation in social science research.

• Afternoon Session: Data Collection and Preprocessing for LLMs
  • Methods for collecting text data relevant to social science research.
  • Preprocessing text data for LLMs: tokenization, handling missing data, and batch processing.
• Case Demonstration:
  • Collection and preprocessing news articles for sentiment analysis using a LLM.
  • Key takeaway: Preparing real-world data for analysis wit LLMs.

Day 3: Advanced Aplications of LLMs in Social Sciences

• Morning Session: Fine-Tuning LLMs for Custom Use-Cases
  • The concept of model fine-tuning and transfer learning.
  • Preparing a dataset for fine-tuning an LLM on a social science-specific task.
  • Initiating a fine-tuning process on a subset of data.
• Case Demonstration:
  • Fine-tuning an LLM to recognize and classify academic articles into social science subfields.
  • Key takeaway: Tailoring LLMs to understand and categorize domain-specific content.

• Afternoon Session: Project Development and Ethical Implications
  • How to design a social science research project using LLMs.
  • Discussion on the ethical implications and potential biases in LLM use.
  • Sharing results responsibly and transparently.
• Case Demonstration:
  • Developing a project outline that uses an LLM to study social narratives in historical newspaper archives.
  • Key takeaway: Constructing a responsible and informative social science research project using LLMs.

• Final Activity: Workshop Wrap-up and Next Steps
  • Participants share their project ideas and receive feedback
  • Resources for further learning and exploration in Python, LLMs and social science research
  • Discuss potential collaborations and future research projects.