Beyond Classification: Financial Reasoning in State-of-the-Art Language Models

by   Guijin Son, et al.

Large Language Models (LLMs), consisting of 100 billion or more parameters, have demonstrated remarkable ability in complex multi-step reasoning tasks. However, the application of such generic advancements has been limited to a few fields, such as clinical or legal, with the field of financial reasoning remaining largely unexplored. To the best of our knowledge, the ability of LLMs to solve financial reasoning problems has never been dealt with, and whether it can be performed at any scale remains unknown. To address this knowledge gap, this research presents a comprehensive investigation into the potential application of LLMs in the financial domain. The investigation includes a detailed exploration of a range of subjects, including task formulation, synthetic data generation, prompting methods, and evaluation capability. Furthermore, the study benchmarks various GPT variants with parameter scales ranging from 2.8B to 13B, with and without instruction tuning, on diverse dataset sizes. By analyzing the results, we reveal that the ability to generate coherent financial reasoning first emerges at 6B parameters, and continues to improve with better instruction-tuning or larger datasets. Additionally, the study provides a publicly accessible dataset named sFIOG (Synthetic-Financial Investment Opinion Generation), consisting of 11,802 synthetic investment thesis samples, to support further research in the field of financial reasoning. Overall, this research seeks to contribute to the understanding of the efficacy of language models in the field of finance, with a particular emphasis on their ability to engage in sophisticated reasoning and analysis within the context of investment decision-making.


page 4

page 6


Large Language Models Encode Clinical Knowledge

Large language models (LLMs) have demonstrated impressive capabilities i...

Instruction Tuning for Large Language Models: A Survey

This paper surveys research works in the quickly advancing field of inst...

FinEval: A Chinese Financial Domain Knowledge Evaluation Benchmark for Large Language Models

Large language models (LLMs) have demonstrated exceptional performance i...

Temporal Data Meets LLM – Explainable Financial Time Series Forecasting

This paper presents a novel study on harnessing Large Language Models' (...

Specializing Smaller Language Models towards Multi-Step Reasoning

The surprising ability of Large Language Models (LLMs) to perform well o...

Textbooks Are All You Need II: phi-1.5 technical report

We continue the investigation into the power of smaller Transformer-base...

Please sign up or login with your details

Forgot password? Click here to reset