PIXIU: A Large Language Model, Instruction Data and Evaluation Benchmark
  for Finance
Qianqian Xie Weiguang Han Xiao Zhang Yanzhao Lai Min Peng Alejandro Lopez-Lira Jimin Huang
Although large language models (LLMs) has shown great performance on natural
language processing (NLP) in the financial domain, there are no publicly
available financial tailtored LLMs, instruction tuning datasets, and evaluation
benchmarks, which is critical for continually pushing forward the open-source
development of financial artificial intelligence (AI). This paper introduces
PIXIU, a comprehensive framework including the first financial LLM based on
fine-tuning LLaMA with instruction data, the first instruction data with 136K
data samples to support the fine-tuning, and an evaluation benchmark with 5
tasks and 9 datasets. We first construct the large-scale multi-task instruction
data considering a variety of financial tasks, financial document types, and
financial data modalities. We then propose a financial LLM called FinMA by
fine-tuning LLaMA with the constructed dataset to be able to follow
instructions for various financial tasks. To support the evaluation of
financial LLMs, we propose a standardized benchmark that covers a set of
critical financial tasks, including five financial NLP tasks and one financial
prediction task. With this benchmark, we conduct a detailed analysis of FinMA
and several existing LLMs, uncovering their strengths and weaknesses in
handling critical financial tasks. The model, datasets, benchmark, and
experimental results are open-sourced to facilitate future research in
financial AI.