  Artificial intelligence is making significant strides in the finance
industry, revolutionizing how data is processed and interpreted. Among these
technologies, large language models (LLMs) have demonstrated substantial
potential to transform financial services by automating complex tasks,
enhancing customer service, and providing detailed financial analysis. Firstly,
we introduce IDEA-FinBench, an evaluation benchmark specifically tailored for
assessing financial knowledge in large language models (LLMs). This benchmark
utilizes questions from two globally respected and authoritative financial
professional exams, aimimg to comprehensively evaluate the capability of LLMs
to directly address exam questions pertinent to the finance sector. Secondly,
we propose IDEA-FinKER, a Financial Knowledge Enhancement framework designed to
facilitate the rapid adaptation of general LLMs to the financial domain,
introducing a retrieval-based few-shot learning method for real-time
context-level knowledge injection, and a set of high-quality financial
knowledge instructions for fine-tuning any general LLM. Finally, we present
IDEA-FinQA, a financial question-answering system powered by LLMs. This system
is structured around a scheme of real-time knowledge injection and factual
enhancement using external knowledge. IDEA-FinQA is comprised of three main
modules: the data collector, the data querying module, and LLM-based agents
tasked with specific functions.
