Financial Knowledge Large Language Model
This work addresses the need for domain-specific AI in finance, offering tools to enhance LLMs for financial tasks, though it appears incremental as it builds on existing LLM methods with domain-specific adaptations.
The authors tackled the problem of adapting large language models (LLMs) to the finance domain by introducing IDEA-FinBench for evaluation, IDEA-FinKER for knowledge enhancement, and IDEA-FinQA for question-answering, resulting in a comprehensive framework to improve financial knowledge and task performance in LLMs.
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.