# REFinD: Relation Extraction Financial Dataset

arXiv: https://arxiv.org/abs/2305.18322 · HTML: https://mlsanity.com/paper/2305.18322/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 23 references that are in the map.
- First posted on arXiv 22 May 2023.

## Builds on (most cited first, 12 of 12)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [SpanBERT: Improving Pre-training by Representing and Predicting Spans](https://mlsanity.com/paper/1907.10529/) (2019)
- [FinBERT: Financial Sentiment Analysis with Pre-trained Language Models](https://mlsanity.com/paper/1908.10063/) (2019)
- [LUKE: Deep Contextualized Entity Representations with Entity-aware Self-attention](https://mlsanity.com/paper/2010.01057/) (2020)
- [Matching the Blanks: Distributional Similarity for Relation Learning](https://mlsanity.com/paper/1906.03158/) (2019)
- [FewRel: A Large-Scale Supervised Few-Shot Relation Classification Dataset with State-of-the-Art Evaluation](https://mlsanity.com/paper/1810.10147/) (2018)
- [WHEN FLUE MEETS FLANG: Benchmarks and Large Pre-trained Language Model for Financial Domain](https://mlsanity.com/paper/2211.00083/) (2022)
- [Learning from Context or Names? An Empirical Study on Neural Relation Extraction](https://mlsanity.com/paper/2010.01923/) (2020)
- [MAVEN-ERE: A Unified Large-scale Dataset for Event Coreference, Temporal, Causal, and Subevent Relation Extraction](https://mlsanity.com/paper/2211.07342/) (2022)
- [Relation Classification via Recurrent Neural Network](https://mlsanity.com/paper/1508.01006/) (2015)
- [CrossRE: A Cross-Domain Dataset for Relation Extraction](https://mlsanity.com/paper/2210.09345/) (2022)

## Built on by (most cited first, 17 of 17)

- [A Survey of Large Language Models for Financial Applications: Progress, Prospects and Challenges](https://mlsanity.com/paper/2406.11903/) (2024)
- [Financial Report Chunking for Effective Retrieval Augmented Generation](https://mlsanity.com/paper/2402.05131/) (2024)
- [Evaluating Large Language Models as Expert Annotators](https://mlsanity.com/paper/2508.07827/) (2025)
- [Bridging Language Models and Financial Analysis](https://mlsanity.com/paper/2503.22693/) (2025)
- [GPT-FinRE: In-context Learning for Financial Relation Extraction using Large Language Models](https://mlsanity.com/paper/2306.17519/) (2023)
- [iMETRE: Incorporating Markers of Entity Types for Relation Extraction](https://mlsanity.com/paper/2307.00132/) (2023)
- [FinTree: Financial Dataset Pretrain Transformer Encoder for Relation Extraction](https://mlsanity.com/paper/2307.13900/) (2023)
- [Ahead of the Text: Leveraging Entity Preposition for Financial Relation Extraction](https://mlsanity.com/paper/2308.04534/) (2023)
- [The CLEF-2026 FinMMEval Lab: Multilingual and Multimodal Evaluation of Financial AI Systems](https://mlsanity.com/paper/2602.10886/) (2026)
- [Enhancing Language Models for Financial Relation Extraction with Named Entities and Part-of-Speech](https://mlsanity.com/paper/2405.06665/) (2024)
- [FinBloom: Knowledge Grounding Large Language Model with Real-time Financial Data](https://mlsanity.com/paper/2502.18471/) (2025)
- [Comparative Analysis of AI Agent Architectures for Entity Relationship Classification](https://mlsanity.com/paper/2506.02426/) (2025)
- [FinRED: An Expert-Guided Benchmark Generation and Evaluation Framework for Financial LLM Red-Teaming](https://mlsanity.com/paper/2606.19887/) (2026)
- [Maximizing Relation Extraction Potential: A Data-Centric Study to Unveil Challenges and Opportunities](https://mlsanity.com/paper/2409.04934/) (2024)
- [A Pluggable Multi-Task Learning Framework for Sentiment-Aware Financial Relation Extraction](https://mlsanity.com/paper/2506.12452/) (2025)
- [DREaM: Drug-Drug Relation Extraction via Transfer Learning Method](https://mlsanity.com/paper/2510.23189/) (2025)
- [Query Generation Pipeline with Enhanced Answerability Assessment for Financial Information Retrieval](https://mlsanity.com/paper/2511.05000/) (2025)

## Abstract (arXiv)

A number of datasets for Relation Extraction (RE) have been created to aide downstream tasks such as information retrieval, semantic search, question answering and textual entailment. However, these datasets fail to capture financial-domain specific challenges since most of these datasets are compiled using general knowledge sources such as Wikipedia, web-based text and news articles, hindering real-life progress and adoption within the financial world. To address this limitation, we propose REFinD, the first large-scale annotated dataset of relations, with ~29K instances and 22 relations amongst 8 types of entity pairs, generated entirely over financial documents. We also provide an empirical evaluation with various state-of-the-art models as benchmarks for the RE task and highlight the challenges posed by our dataset. We observed that various state-of-the-art deep learning models struggle with numeric inference, relational and directional ambiguity.