dynamic-fewshot-learning

This repository contains the code for the paper [Dynamic Few-shot Learning for Computational Social Science].

Code Structure

The code for this project is organized as follows:

• benchmarks/ contains the code for the benchmark datasets used in the paper. This folder also contains the code for the data preprocessing and the code for the LLM analysis.
• isca/ contains the code for the tools and utils needed to run the experiments.
• benchmarks/data/ contains the data for the benchmark datasets used in the paper.
• benchmarks/prep.ipynb contains the code for the data preprocessing. (not needed to run the experiments)

Experiment run

To run the experiments, you can use the following command in the terminal:

cd benchmarks
python <benchmark_name>.py --model_name <model_name> --temperature <temperature> --max_tokens <max_tokens>

where <benchmark_name> is the name of the benchmark dataset, <model_name> is the name of the model, <temperature> is the temperature value (default is 0), and <max_tokens> is the maximum number of tokens (default is 100).