# Zero- and Few-Shots Knowledge Graph Triplet Extraction with Large Language Models

arXiv: https://arxiv.org/abs/2312.01954 · HTML: https://mlsanity.com/paper/2312.01954/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 35 references that are in the map.
- First posted on arXiv 4 Dec 2023.

## Builds on (most cited first, 13 of 13)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [LLaMA: Open and Efficient Foundation Language Models](https://mlsanity.com/paper/2302.13971/) (2023)
- [Deep reinforcement learning from human preferences](https://mlsanity.com/paper/1706.03741/) (2017)
- [MiniLM: Deep Self-Attention Distillation for Task-Agnostic Compression of Pre-Trained Transformers](https://mlsanity.com/paper/2002.10957/) (2020)
- [The RefinedWeb Dataset for Falcon LLM: Outperforming Curated Corpora with Web Data, and Web Data Only](https://mlsanity.com/paper/2306.01116/) (2023)
- [ChatIE: Zero-Shot Information Extraction via Chatting with ChatGPT](https://mlsanity.com/paper/2302.10205/) (2023)
- [A Survey on Recent Advances in Named Entity Recognition from Deep Learning models](https://mlsanity.com/paper/1910.11470/) (2019)
- [Joint Extraction of Entities and Relations Based on a Novel Tagging Scheme](https://mlsanity.com/paper/1706.05075/) (2017)
- [RelationPrompt: Leveraging Prompts to Generate Synthetic Data for Zero-Shot Relation Triplet Extraction](https://mlsanity.com/paper/2203.09101/) (2022)
- [GenIE: Generative Information Extraction](https://mlsanity.com/paper/2112.08340/) (2021)
- [UniRel: Unified Representation and Interaction for Joint Relational Triple Extraction](https://mlsanity.com/paper/2211.09039/) (2022)
- [Deep Neural Approaches to Relation Triplets Extraction: A Comprehensive Survey](https://mlsanity.com/paper/2103.16929/) (2021)
- [Zero-shot Triplet Extraction by Template Infilling](https://mlsanity.com/paper/2212.10708/) (2022)

## Built on by (most cited first, 7 of 7)

- [LLM-empowered knowledge graph construction: A survey](https://mlsanity.com/paper/2510.20345/) (2025)
- [WESE: Weak Exploration to Strong Exploitation for LLM Agents](https://mlsanity.com/paper/2404.07456/) (2024)
- [LKD-KGC: Domain-Specific KG Construction via LLM-driven Knowledge Dependency Parsing](https://mlsanity.com/paper/2505.24163/) (2025)
- [Connect the Dots: Knowledge Graph-Guided Crawler Attack on Retrieval-Augmented Generation Systems](https://mlsanity.com/paper/2601.15678/) (2026)
- [LLM-based Triplet Extraction from Financial Reports](https://mlsanity.com/paper/2602.11886/) (2026)
- [Performance Evaluation of LLMs in Automated RDF Knowledge Graph Generation](https://mlsanity.com/paper/2603.29878/) (2026)
- [GenAI-Driven Approach to RISC-V Supply Chain Exploration](https://mlsanity.com/paper/2605.15223/) (2026)

## Abstract (arXiv)

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.