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.

翻译：大型语言模型（LLMs）拥有超过1000亿参数，在复杂的多步推理任务中展现出卓越能力。然而，此类通用进展的应用仅局限于临床或法律等少数领域，金融推理领域在很大程度上仍未被探索。据我们所知，LLMs解决金融推理问题的能力从未被研究过，且其能否以任意规模实现仍属未知。为填补这一知识空白，本研究系统探讨了LLMs在金融领域的潜在应用。研究涵盖主题的详细探索，包括任务制定、合成数据生成、提示方法及评估能力。此外，研究还对参数规模从2.8B到13B的各种GPT变体进行了基准测试，比较了有无指令微调及不同数据集规模下的表现。通过分析结果，我们发现生成连贯金融推理的能力最初在6B参数时显现，并通过更好的指令微调或更大数据集持续提升。研究还提供了一个名为sFIOG（合成金融投资意见生成）的公开数据集，包含11,802个合成投资主题样本，以支持金融推理领域的进一步研究。总体而言，本研究旨在增进对语言模型在金融领域效用的理解，尤其侧重于其在投资决策中进行复杂推理与分析的能力。