Several large-scale datasets (e.g., WikiSQL, Spider) for developing natural language interfaces to databases have recently been proposed. These datasets cover a wide breadth of domains but fall short on some essential domains, such as finance and accounting. Given that accounting databases are used worldwide, particularly by non-technical people, there is an imminent need to develop models that could help extract information from accounting databases via natural language queries. In this resource paper, we aim to fill this gap by proposing a new large-scale Text-to-SQL dataset for the accounting and financial domain: BookSQL. The dataset consists of 100k natural language queries-SQL pairs, and accounting databases of 1 million records. We experiment with and analyze existing state-of-the-art models (including GPT-4) for the Text-to-SQL task on BookSQL. We find significant performance gaps, thus pointing towards developing more focused models for this domain.

The financial industry presents unique challenges and opportunities for Natural Language Processing In this paper, we propose a comprehensive approach (NLP) models (Huang et al., 2020). Traditional to build Chinese financial LLMs and present financial NLP models have made progress DISC-FinLLM. Our method aims to enhance general in various tasks such as news sentiment analysis LLMs by equipping them with the skills to (Araci, 2019), financial event extraction (Zheng address typical needs for financial text generation et al., 2019; Yang et al., 2019), financial report and understanding, meaningful multi-turn conversations generation (Chapman et al., 2022), stock price prediction on financial topics, and plugin functionality (Chen et al., 2018) and financial text summarization to support financial modeling and knowledgeenhanced (La Quatra and Cagliero, 2020).

The emergence of Large Language Models (LLMs), such as ChatGPT, has revolutionized general natural language preprocessing (NLP) tasks. However, their expertise in the financial domain lacks a comprehensive evaluation. To assess the ability of LLMs to solve financial NLP tasks, we present FinLMEval, a framework for Financial Language Model Evaluation, comprising nine datasets designed to evaluate the performance of language models. This study compares the performance of encoder-only language models and the decoder-only language models. Our findings reveal that while some decoder-only LLMs demonstrate notable performance across most financial tasks via zero-shot prompting, they generally lag behind the fine-tuned expert models, especially when dealing with proprietary datasets. We hope this study provides foundation evaluations for continuing efforts to build more advanced LLMs in the financial domain.

At the beginning era of large language model, it is quite critical to generate a high-quality financial dataset to fine-tune a large language model for financial related tasks. Thus, this paper presents a carefully designed data creation pipeline for this purpose. Particularly, we initiate a dialogue between an AI investor and financial expert using ChatGPT and incorporate the feedback of human financial experts, leading to the refinement of the dataset. This pipeline yielded a robust instruction tuning dataset comprised of 103k multi-turn chats. Extensive experiments have been conducted on this dataset to evaluate the model's performance by adopting an external GPT-4 as the judge. The promising experimental results verify that our approach led to significant advancements in generating accurate, relevant, and financial-style responses from AI models, and thus providing a powerful tool for applications within the financial sector.

With the rise of chat bot assistants and the rise of robo-advisers, the future of tech -- especially FinTech -- looks like a personalized one. But not all companies are jumping on the bot bandwagon just yet. Vancouver-based Wealthbar, which provides online investment portfolios and financial planning services, has raised 5.5 million in funding to improve the client experience with Wealthbar's team of financial experts. The funding was led by Nicola Wealth Management, one of Canada's biggest high-net-worth wealth management company with 3 billion in assets under management, and Howard Atkinson, an ETF industry pioneer and former President of Horizon ETFs. Wealthbar's co-founder and CEO, Tea Nicola, said that the company is looking to provide a more personalized client experience.