# How Good are LLMs at Relation Extraction under Low-Resource Scenario? Comprehensive Evaluation

arXiv: https://arxiv.org/abs/2406.11162 · HTML: https://mlsanity.com/paper/2406.11162/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 37 references that are in the map.
- First posted on arXiv 17 Jun 2024.

## Builds on (most cited first, 14 of 14)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [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)
- [No Language Left Behind: Scaling Human-Centered Machine Translation](https://mlsanity.com/paper/2207.04672/) (2022)
- [The RefinedWeb Dataset for Falcon LLM: Outperforming Curated Corpora with Web Data, and Web Data Only](https://mlsanity.com/paper/2306.01116/) (2023)
- [OLMo: Accelerating the Science of Language Models](https://mlsanity.com/paper/2402.00838/) (2024)
- [FewRel: A Large-Scale Supervised Few-Shot Relation Classification Dataset with State-of-the-Art Evaluation](https://mlsanity.com/paper/1810.10147/) (2018)
- [Connecting the Dots: Document-level Neural Relation Extraction with Edge-oriented Graphs](https://mlsanity.com/paper/1909.00228/) (2019)
- [Improved Neural Relation Detection for Knowledge Base Question Answering](https://mlsanity.com/paper/1704.06194/) (2017)
- [Improving Relation Extraction by Pre-trained Language Representations](https://mlsanity.com/paper/1906.03088/) (2019)
- [CrossRE: A Cross-Domain Dataset for Relation Extraction](https://mlsanity.com/paper/2210.09345/) (2022)
- [Relation Schema Induction using Tensor Factorization with Side Information](https://mlsanity.com/paper/1605.04227/) (2016)
- [Prompt-Learning for Cross-Lingual Relation Extraction](https://mlsanity.com/paper/2304.10354/) (2023)
- [Weakly-Supervised Questions for Zero-Shot Relation Extraction](https://mlsanity.com/paper/2301.09640/) (2023)

## Built on by (most cited first, 9 of 9)

- [GraphEval: A Lightweight Graph-Based LLM Framework for Idea Evaluation](https://mlsanity.com/paper/2503.12600/) (2025)
- [SLIDE: Sliding Localized Information for Document Extraction](https://mlsanity.com/paper/2503.17952/) (2025)
- [LPCAN: Lightweight Pyramid Cross-Attention Network for Rail Surface Defect Detection Using RGB-D Data](https://mlsanity.com/paper/2601.09118/) (2026)
- [Knowledge-Embedded and Hypernetwork-Guided Few-Shot Substation Meter Defect Image Generation Method](https://mlsanity.com/paper/2601.09238/) (2026)
- [MA-LipNet: Multi-Dimensional Attention Networks for Robust Lipreading](https://mlsanity.com/paper/2601.20881/) (2026)
- [The Diffusion Duet: Harmonizing Dual Channels with Wavelet Suppression for Image Separation](https://mlsanity.com/paper/2602.13361/) (2026)
- [Detecting Deepfakes with Multivariate Soft Blending and CLIP-based Image-Text Alignment](https://mlsanity.com/paper/2602.15903/) (2026)
- [Efficient Image Super-Resolution with Multi-Scale Spatial Adaptive Attention Networks](https://mlsanity.com/paper/2603.00118/) (2026)
- [A Deformable Attention-Based Detection Transformer with Cross-Scale Feature Fusion for Industrial Coil Spring Inspection](https://mlsanity.com/paper/2603.13429/) (2026)

## Abstract (arXiv)

Relation Extraction (RE) serves as a crucial technology for transforming unstructured text into structured information, especially within the framework of Knowledge Graph development. Its importance is emphasized by its essential role in various downstream tasks. Besides the conventional RE methods which are based on neural networks and pre-trained language models, large language models (LLMs) are also utilized in the research field of RE. However, on low-resource languages (LRLs), both conventional RE methods and LLM-based methods perform poorly on RE due to the data scarcity issues. To this end, this paper constructs low-resource relation extraction datasets in 10 LRLs in three regions (Central Asia, Southeast Asia and Middle East). The corpora are constructed by translating the original publicly available English RE datasets (NYT10, FewRel and CrossRE) using an effective multilingual machine translation. Then, we use the language perplexity (PPL) to filter out the low-quality data from the translated datasets. Finally, we conduct an empirical study and validate the performance of several open-source LLMs on these generated LRL RE datasets.