The application of Large Language Models (LLMs), particularly those with hundreds of billions of parameters, holds significant potential in the realm of financial document generation and evaluation. These models excel at summarizing vast amounts of data, offering insights into individual companies or entire industries. Their ability to quickly process and synthesize information can streamline the creation of high-level overviews, such as executive summaries, industry reports, and market trend analyses.
However, while LLMs are strong in summarization, they still face challenges in tasks requiring complex reasoning or induction, such as the evaluation of intricate financial statements or legal documents. Currently, reports that require only a high-level perspective—free from deep analytical or inductive tasks—are ideal for LLM applications. As these models continue to evolve, their stability and reasoning capabilities may improve, expanding their use in more detailed financial evaluations.