# A survey on cutting-edge relation extraction techniques based on language models

arXiv: https://arxiv.org/abs/2411.18157 · HTML: https://mlsanity.com/paper/2411.18157/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Nov 2024.

## Built on by (most cited first, 3 of 3)

- [Applications of the Transformer Architecture in AI-Assisted English Reading Comprehension](https://mlsanity.com/paper/2604.23615/) (2026)
- [Few-Shot Biomedical Relation Extraction with Large Language Models: A Viable Alternative to Supervised Learning?](https://mlsanity.com/paper/2606.15412/) (2026)
- [CVE-TTP KG: Knowledge Graph Linking Software Vulnerabilities to Attack Behaviors](https://mlsanity.com/paper/2606.31557/) (2026)

## Abstract (arXiv)

This comprehensive survey delves into the latest advancements in Relation Extraction (RE), a pivotal task in natural language processing essential for applications across biomedical, financial, and legal sectors. This study highlights the evolution and current state of RE techniques by analyzing 137 papers presented at the Association for Computational Linguistics (ACL) conferences over the past four years, focusing on models that leverage language models. Our findings underscore the dominance of BERT-based methods in achieving state-of-the-art results for RE while also noting the promising capabilities of emerging large language models (LLMs) like T5, especially in few-shot relation extraction scenarios where they excel in identifying previously unseen relations.