# Financial Named Entity Recognition: How Far Can LLM Go?

arXiv: https://arxiv.org/abs/2501.02237 · HTML: https://mlsanity.com/paper/2501.02237/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 18 references that are in the map.
- First posted on arXiv 4 Jan 2025.

## Builds on (most cited first, 8 of 8)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [The Llama 3 Herd of Models](https://mlsanity.com/paper/2407.21783/) (2024)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [Deep reinforcement learning from human preferences](https://mlsanity.com/paper/1706.03741/) (2017)
- [A Survey on In-context Learning](https://mlsanity.com/paper/2301.00234/) (2022)
- [Large Language Models in Finance: A Survey](https://mlsanity.com/paper/2311.10723/) (2023)
- [Large Language Models Meet NLP: A Survey](https://mlsanity.com/paper/2405.12819/) (2024)
- [Are ChatGPT and GPT-4 General-Purpose Solvers for Financial Text Analytics? A Study on Several Typical Tasks](https://mlsanity.com/paper/2305.05862/) (2023)

## Built on by (most cited first, 8 of 8)

- [Finance Language Model Evaluation (FLaME)](https://mlsanity.com/paper/2506.15846/) (2025)
- [FiNERweb: Datasets and Artifacts for Scalable Multilingual Named Entity Recognition](https://mlsanity.com/paper/2512.13884/) (2025)
- [Instruction Finetuning DeepSeek-R1-8B Model Using LoRA and NEFTune](https://mlsanity.com/paper/2606.10392/) (2026)
- [Reliable Financial Named Entity Recognition Under Domain Shift: Confidence Estimation and Selective Prediction](https://mlsanity.com/paper/2608.19558/) (2026)
- [Instruction Finetuning LLaMA-3-8B Model Using LoRA for Financial Named Entity Recognition](https://mlsanity.com/paper/2601.10043/) (2026)
- [A Hybrid Method for Low-Resource Named Entity Recognition](https://mlsanity.com/paper/2605.04489/) (2026)
- [From Information to Delegation: Mapping Human-AI Financial Decision Making](https://mlsanity.com/paper/2608.02100/) (2026)
- [Fin_Agent at FinEval 2026: Task-Aware Methods for Multilingual Financial NLP](https://mlsanity.com/paper/2610.14101/) (2026)

## Abstract (arXiv)

The surge of large language models (LLMs) has revolutionized the extraction and analysis of crucial information from a growing volume of financial statements, announcements, and business news. Recognition for named entities to construct structured data poses a significant challenge in analyzing financial documents and is a foundational task for intelligent financial analytics. However, how effective are these generic LLMs and their performance under various prompts are yet need a better understanding. To fill in the blank, we present a systematic evaluation of state-of-the-art LLMs and prompting methods in the financial Named Entity Recognition (NER) problem. Specifically, our experimental results highlight their strengths and limitations, identify five representative failure types, and provide insights into their potential and challenges for domain-specific tasks.