Large language models (LLMs) excel at generating human-like responses but often struggle with interactive tasks that require access to real-time information. This limitation poses challenges in finance, where models must access up-to-date information, such as recent news or price movements, to support decision-making. To address this, we introduce Financial Agent, a knowledge-grounding approach for LLMs to handle financial queries using real-time text and tabular data. Our contributions are threefold: First, we develop a Financial Context Dataset of over 50,000 financial queries paired with the required context. Second, we develop FinBloom 7B, a custom 7 billion parameter LLM, by fine-tuning Bloom 7B on 14 million financial news articles from Reuters and Deutsche Presse-Agentur (DPA), alongside a random sample of 25% from 12 million Securities and Exchange Commission (SEC) filings. Third, we fine-tune FinBloom 7B using the Financial Context Dataset to serve as a Financial Agent. This agent generates relevant financial context, enabling efficient real-time data retrieval to answer user queries. By reducing latency and eliminating the need for users to manually provide accurate data, our approach significantly enhances the capability of LLMs to handle dynamic financial tasks. Our proposed approach makes real-time financial decisions, algorithmic trading and other related tasks streamlined, and is valuable in contexts with high-velocity data flows.

翻译：大语言模型（LLM）在生成类人响应方面表现出色，但在需要访问实时信息的交互式任务中往往存在困难。这一局限在金融领域尤为突出，因为模型必须获取最新信息（如近期新闻或价格变动）以支持决策。为此，我们提出了金融智能体（Financial Agent），一种基于实时文本与表格数据的知识落地方法，使LLM能够处理金融查询。我们的贡献包括三个方面：首先，我们构建了一个包含超过50,000条金融查询及其对应所需上下文的金融上下文数据集。其次，我们基于Bloom 7B模型，在来自路透社和德新社（DPA）的1,400万篇金融新闻文章，以及从1,200万份美国证券交易委员会（SEC）文件中随机抽取的25%样本上进行微调，开发了定制化的70亿参数大语言模型FinBloom 7B。最后，我们利用金融上下文数据集对FinBloom 7B进行微调，使其作为金融智能体运行。该智能体能够生成相关的金融上下文，从而实现高效的实时数据检索以回答用户查询。通过降低延迟并免除用户手动提供准确数据的需要，我们的方法显著提升了LLM处理动态金融任务的能力。所提出的方法使实时金融决策、算法交易及其他相关任务流程得以优化，在高流速数据环境中具有重要应用价值。