This paper introduces a benchmark for evaluating the ability of large language model (LLM) agents to forecast earnings based on financial data. Practitioners in finance and AI research may care about this work because it could lead to more accurate and reliable earnings forecasting models.
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This paper introduces a benchmark, called Investor's Last Exam (ILE), to evaluate the ability of AI agents to make informed investment decisions without having access to future information. Practitioners in the field of artificial intelligence, finance, and investment management may care about this research as it aims to improve the performance of financial agents in making investment judgments.