Financial Question Answering

Financial question answering (FinQA) focuses on developing AI systems capable of accurately answering complex questions requiring financial knowledge and numerical reasoning, often involving diverse data types like text, tables, and charts. Current research emphasizes creating robust benchmarks to evaluate models, particularly large language models (LLMs), and exploring techniques like retrieval-augmented generation and case-based reasoning to improve their performance on tasks involving multi-step reasoning and long-form documents. These advancements hold significant potential for improving financial analysis, automating tasks, and enhancing transparency and efficiency in financial markets.