For a financial analyst, the question and answer (Q\&A) segment of the
company financial report is a crucial piece of information for various analysis
and investment decisions. However, extracting valuable insights from the Q\&A
section has posed considerable challenges as the conventional methods such as
detailed reading and note-taking lack scalability and are susceptible to human
errors, and Optical Character Recognition (OCR) and similar techniques
encounter difficulties in accurately processing unstructured transcript text,
often missing subtle linguistic nuances that drive investor decisions. Here, we
demonstrate the utilization of Large Language Models (LLMs) to efficiently and
rapidly extract information from earnings report transcripts while ensuring
high accuracy transforming the extraction process as well as reducing
hallucination by combining retrieval-augmented generation technique as well as
metadata. We evaluate the outcomes of various LLMs with and without using our
proposed approach based on various objective metrics for evaluating Q\&A
systems, and empirically demonstrate superiority of our method.

利用大型语言模型提取财务分析师关键信息，通过结合检索增强生成技术和元数据减少幻觉，高效地从公司财报问答部分提取准确信息。