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

• URL: http://arxiv.org/abs/2310.04793v2
• Date: Sat, 11 Nov 2023 06:51:24 GMT
• Title: FinGPT: Instruction Tuning Benchmark for Open-Source Large Language Models in Financial Datasets
• Authors: Neng Wang, Hongyang Yang, Christina Dan Wang
• Abstract summary: This paper introduces a distinctive approach anchored in the Instruction Tuning paradigm for open-source large language models. We capitalize on the interoperability of open-source models, ensuring a seamless and transparent integration. The paper presents a benchmarking scheme designed for end-to-end training and testing, employing a cost-effective progression.
• Score: 9.714447724811842
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In the swiftly expanding domain of Natural Language Processing (NLP), the potential of GPT-based models for the financial sector is increasingly evident. However, the integration of these models with financial datasets presents challenges, notably in determining their adeptness and relevance. This paper introduces a distinctive approach anchored in the Instruction Tuning paradigm for open-source large language models, specifically adapted for financial contexts. Through this methodology, we capitalize on the interoperability of open-source models, ensuring a seamless and transparent integration. We begin by explaining the Instruction Tuning paradigm, highlighting its effectiveness for immediate integration. The paper presents a benchmarking scheme designed for end-to-end training and testing, employing a cost-effective progression. Firstly, we assess basic competencies and fundamental tasks, such as Named Entity Recognition (NER) and sentiment analysis to enhance specialization. Next, we delve into a comprehensive model, executing multi-task operations by amalgamating all instructional tunings to examine versatility. Finally, we explore the zero-shot capabilities by earmarking unseen tasks and incorporating novel datasets to understand adaptability in uncharted terrains. Such a paradigm fortifies the principles of openness and reproducibility, laying a robust foundation for future investigations in open-source financial large language models (FinLLMs).

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