用大模型生成金融时间序列报告，自动区分数据、推理与外部知识来源。
AI Analyst: Framework and Comprehensive Evaluation of Large Language Models for Financial Time Series Report Generation
- 构建框架整合提示工程、模型选择与评估，提升报告生成质量。
- 在真实与合成数据上验证模型可生成连贯且信息丰富的报告。
- 引入自动化标注系统，精准识别报告中三类信息来源，便于评估事实性。
本文探索大语言模型（LLMs）从时间序列数据生成金融报告的潜力。我们提出一个包含提示工程、模型选择与评估的框架，并引入自动化突出显示系统，对生成报告中的信息进行分类：直接源自时间序列数据、基于金融推理或依赖外部知识。该方法有助于评估模型的事实准确性和推理能力。实验使用真实股票市场指数数据与合成时间序列数据，证明了LLMs能够生成连贯且富含信息的金融报告。
原文摘要 · Abstract (English)
This paper explores the potential of large language models (LLMs) to generate financial reports from time series data. We propose a framework encompassing prompt engineering, model selection, and evaluation. We introduce an automated highlighting system to categorize information within the generated reports, differentiating between insights derived directly from time series data, stemming from financial reasoning, and those reliant on external knowledge. This approach aids in evaluating the factual grounding and reasoning capabilities of the models. Our experiments, utilizing both data from the real stock market indices and synthetic time series, demonstrate the capability of LLMs to produce coherent and informative financial reports.
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