In this work, we tested the Triplet Extraction (TE) capabilities of a variety of Large Language Models (LLMs) of different sizes in the Zero- and Few-Shots settings. In detail, we proposed a pipeline that dynamically gathers contextual information from a Knowledge Base (KB), both in the form of context triplets and of (sentence, triplets) pairs as examples, and provides it to the LLM through a prompt. The additional context allowed the LLMs to be competitive with all the older fully trained baselines based on the Bidirectional Long Short-Term Memory (BiLSTM) Network architecture. We further conducted a detailed analysis of the quality of the gathered KB context, finding it to be strongly correlated with the final TE performance of the model. In contrast, the size of the model appeared to only logarithmically improve the TE capabilities of the LLMs.
@article{arxiv.2312.01954,
title = {Zero- and Few-Shots Knowledge Graph Triplet Extraction with Large Language Models},
author = {Andrea Papaluca and Daniel Krefl and Sergio Mendez Rodriguez and Artem Lensky and Hanna Suominen},
journal= {arXiv preprint arXiv:2312.01954},
year = {2023}
}