mlsanityData to 14 Oct 2026

GPT-FinRE: In-context Learning for Financial Relation Extraction using Large Language Models

arXiv 2306.17519 ↗First posted 30 Jun 2023cs.CLcited by 3

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Abstract (arXiv)

Relation extraction (RE) is a crucial task in natural language processing (NLP) that aims to identify and classify relationships between entities mentioned in text. In the financial domain, relation extraction plays a vital role in extracting valuable information from financial documents, such as news articles, earnings reports, and company filings. This paper describes our solution to relation extraction on one such dataset REFinD. The dataset was released along with shared task as a part of the Fourth Workshop on Knowledge Discovery from Unstructured Data in Financial Services, co-located with SIGIR 2023. In this paper, we employed OpenAI models under the framework of in-context learning (ICL). We utilized two retrieval strategies to find top K relevant in-context learning demonstrations / examples from training data for a given test example. The first retrieval mechanism, we employed, is a learning-free dense retriever and the other system is a learning-based retriever. We were able to achieve 3rd rank overall. Our best F1-score is 0.718.

Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 18 of its 20 references that are in the map. First posted on arXiv 30 Jun 2023.

Builds on

18 of 20 references.

References · 18

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
  2. 2020Language Models are Few-Shot Learnerscited by 32,419
  3. 2019Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformercited by 14,599
  4. 2019ALBERT: A Lite BERT for Self-supervised Learning of Language Representationscited by 2,790
  5. 2017Billion-scale similarity search with GPUscited by 2,497
  6. 2020The Pile: An 800GB Dataset of Diverse Text for Language Modelingcited by 2,411
  7. 2022Rethinking the Role of Demonstrations: What Makes In-Context Learning Work?cited by 1,434
  8. 2019SciBERT: A Pretrained Language Model for Scientific Textcited by 1,414
  9. 2022LaMDA: Language Models for Dialog Applicationscited by 1,220
  10. 2021What Makes Good In-Context Examples for GPT-3?cited by 1,195
  11. 2021Learning To Retrieve Prompts for In-Context Learningcited by 648
  12. 2022Contrastive Decoding: Open-ended Text Generation as Optimizationcited by 609
6 more
  1. 2023Larger language models do in-context learning differentlycited by 394
  2. 2021Constrained Language Models Yield Few-Shot Semantic Parserscited by 163
  3. 2022Self-Adaptive In-Context Learning: An Information Compression Perspective for In-Context Example Selection and Orderingcited by 161
  4. 2023Compositional Exemplars for In-context Learningcited by 144
  5. 2023GPT-RE: In-context Learning for Relation Extraction using Large Language Modelscited by 62
  6. 2023REFinD: Relation Extraction Financial Datasetcited by 17

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Citing papers per year: 2024: 2; 2025: 1.

3 papers.

  1. 2024A Survey of Large Language Models for Financial Applications: Progress, Prospects and Challengescited by 90
  2. 2025Comparative Analysis of AI Agent Architectures for Entity Relationship Classificationcited by 1
  3. 2024CRE-LLM: A Domain-Specific Chinese Relation Extraction Framework with Fine-tuned Large Language Modelcited by 0