# FinGPT: Instruction Tuning Benchmark for Open-Source Large Language Models in Financial Datasets

**Authors:** Neng Wang, Hongyang Yang, Christina Dan Wang  
**arXiv:** [2310.04793](https://arxiv.org/abs/2310.04793)  
**Date:** 2023-10-07  
**Summary page:** https://www.summarizepaper.com/en/paper/2310.04793v2/

## Key points

- Utilization of GPT-based models in the financial sector shows promise
- Challenges in integrating these models with financial datasets
- Introduction of a novel approach based on Instruction Tuning paradigm for open-source large language models tailored for financial applications
- Methodology ensures seamless and transparent integration process by leveraging interoperability of open-source models
- Benchmarking scheme presented for comprehensive training and testing using a cost-effective progression strategy
- Evaluation of basic competencies and essential tasks such as Named Entity Recognition (NER) and sentiment analysis to enhance model specialization
- Exploration of more advanced model performing multi-task operations by amalgamating all instructional tunings to assess versatility
- Delving into zero-shot capabilities by addressing unseen tasks and incorporating new datasets to evaluate adaptability in unexplored domains
- Reinforcement of principles of openness and reproducibility, establishing a robust foundation for future research endeavors in open-source financial large language models (FinLLMs)
- Presentation of pioneering approach titled "FinGPT: Instruction Tuning Benchmark for Open-Source Large Language Models in Financial Datasets" at NeurIPS 2023 Workshop on Instruction Tuning and Instruction Following

## Summary

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In the rapidly evolving field of Natural Language Processing (NLP), the utilization of GPT-based models in the financial sector has shown great promise. However, integrating these models with financial datasets poses challenges in assessing their effectiveness and applicability. This paper introduces a novel approach based on the Instruction Tuning paradigm tailored for open-source large language models, specifically designed for financial applications. By leveraging the interoperability of open-source models, this methodology ensures a seamless and transparent integration process. The paper begins by elucidating the Instruction Tuning paradigm, emphasizing its efficacy for immediate implementation. A benchmarking scheme is then presented for comprehensive training and testing, utilizing a cost-effective progression strategy. Initially, basic competencies and essential tasks such as Named Entity Recognition (NER) and sentiment analysis are evaluated to enhance model specialization. Subsequently, a more advanced model is explored, performing multi-task operations by amalgamating all instructional tunings to assess versatility. Finally, the study delves into zero-shot capabilities by addressing unseen tasks and incorporating new datasets to evaluate adaptability in unexplored domains. This paradigm reinforces principles of openness and reproducibility, establishing a robust foundation for future research endeavors in open-source financial large language models (FinLLMs). Authored by Neng Wang, Hongyang Yang, and Christina Dan Wang, this paper titled "FinGPT: Instruction Tuning Benchmark for Open-Source Large Language Models in Financial Datasets" presents a pioneering approach that holds significant potential for enhancing NLP applications within the financial industry. The work was presented at the Workshop on Instruction Tuning and Instruction Following at NeurIPS 2023.