This repository contains code and data for experiments done in the paper "Using LLMs for the Extraction and Normalization of Product Attribute Values" to be presented at ADBIS 2024. Further information on the benchmark WDC Product Attribute-Value Extraction (WDC Pave) can be found on the project page.
We evaluate hosted LLMs, such as GPT-3.5 and GPT-4.
Therefore, an OpenAI access tokens needs to be placed in a .env file at the root of the repository.
To obtain this OpenAI access token, users must sign up for an OpenAI account.
The codebase requires python 3.9 To install dependencies we suggest to use a conda virtual environment:
conda create -n wdc-pave python=3.9
conda activate wdc-pave
pip install -r requirements.txt
pip install .
The extraction and extraction with normalization WDC PAVE data can be found in the data/processed_datasets folder.
We experiment with various prompt templates involving descriptions and example values, and adding demonstrations. The following figure shows the prompt structures for the two schema descriptions (black font for extraction, black + red font for extraction + normalization).
The prompts and the code to execute the prompts are defined in the folder prompts.
You can run the prompts with the following scripts:
scripts/01_run_example_values_prompts.sh
scripts/02_run_prompts_with_training_data.sh
scripts/08_run_prompts_for_extraction_with_normalization.sh
scripts/10_run_prompts_for_normalization_multiple_attributes.sh
