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Although large language models (LLMs) has shown great performance on naturallanguage processing (NLP) in the financial domain, there are no publiclyavailable financial tailtored LLMs, instruction tuning datasets, and evaluationbenchmarks, which is critical for continually pushing forward the open-sourcedevelopment of financial artificial intelligence (AI). This paper introducesPIXIU, a comprehensive framework including the first financial LLM based onfine-tuning LLaMA with instruction data, the first instruction data with 136Kdata samples to support the fine-tuning, and an evaluation benchmark with 5tasks and 9 datasets. We first construct the large-scale multi-task instructiondata considering a variety of financial tasks, financial document types, andfinancial data modalities. We then propose a financial LLM called FinMA byfine-tuning LLaMA with the constructed dataset to be able to followinstructions for various financial tasks. To support the evaluation offinancial LLMs, we propose a standardized benchmark that covers a set ofcritical financial tasks, including five financial NLP tasks and one financialprediction task. With this benchmark, we conduct a detailed analysis of FinMAand several existing LLMs, uncovering their strengths and weaknesses inhandling critical financial tasks. The model, datasets, benchmark, andexperimental results are open-sourced to facilitate future research infinancial AI.

PIXIU: A Large Language Model, Instruction Data and Evaluation Benchmark for Finance