标题： 思维程序用于金融推理：利用动态上下文示例和生成检索 显示英文标题

标题： Program of Thoughts for Financial Reasoning: Leveraging Dynamic In-Context Examples and Generative Retrieval

摘要： 尽管大型语言模型（LLMs）的能力持续提升，数值推理仍然是一个具有挑战性的领域。像思维链提示、思维树提示和思维程序提示这样的技术可以引导LLMs通过中间推理步骤。虽然少样本提示的上下文学习提高了性能，但LLMs在财务数值推理数据集如FinQA和ConvFinQA上仍落后于最先进模型。在本工作中，我们引入了FINDER，一种新的两步框架，以增强LLMs在财务数值推理方面的能力。第一步利用生成检索器从非结构化数据中提取相关事实，包括文本和表格。随后是上下文感知的思维程序提示，并动态选择上下文示例。我们的模型FINDER在FinQA和ConvFinQA数据集上达到了新的最先进性能，分别超过了之前的基准，执行准确率提高了5.98%和4.05%。 显示英文摘要

摘要： Despite continuous advancements in the capabilities of large language models (LLMs), numerical reasoning remains a challenging area. Techniques like chain-of-thought prompting, tree-of-thought prompting, and program-of-thought prompting guide LLMs through intermediate reasoning steps. Although in-context learning with few-shot prompting has improved performance, LLMs still lag behind state-of-the-art models on financial numerical reasoning datasets such as FinQA and ConvFinQA. In this work, we introduce FINDER, a novel two-step framework, to enhance LLMs' capabilities in financial numerical reasoning. The first step utilizes a generative retriever to extract relevant facts from unstructured data, including both text and tables. This is followed by context-aware Program of Thought prompting with dynamic selection of in-context examples. Our model FINDER achieves a new state-of-the-art performance on both the FinQA and ConvFinQA datasets, surpassing previous benchmarks with execution accuracy improvements of 5.98% and 4.05%, respectively.