金融推理的程序思维：利用动态上下文示例与生成式检索
Program of Thoughts for Financial Reasoning: Leveraging Dynamic In-Context Examples and Generative Retrieval
AI总结：
本文提出FINDER两步框架，结合生成式检索与动态上下文示例的程序思维提示，提升LLM金融数值推理，在FinQA和ConvFinQA上实现新最先进性能。
摘要：
尽管大型语言模型（LLMs）的能力持续进步，数值推理仍是一个具有挑战性的领域。链式思维提示、树状思维提示和程序思维提示等技术引导LLMs通过中间推理步骤。虽然少样本提示的上下文学习提升了性能，但在FinQA和ConvFinQA等金融数值推理数据集上，LLMs仍落后于最先进的模型。在本工作中，我们引入了FINDER，一种新颖的两步框架，以增强LLMs在金融数值推理中的能力。第一步利用生成式检索器从非结构化数据（包括文本和表格）中提取相关事实。随后，结合上下文感知的程序思维提示，动态选择上下文示例。我们的模型FINDER在FinQA和ConvFinQA数据集上均取得了新的最先进性能，执行准确率分别提升了5.98%和4.05%，超越了之前的基准。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
