基于大模型实现因果推断的探讨

Discussion on Causal Inference via Large Model

大模型是利用海量数据形成庞大参数量的机器学习模型,而因果推断是推断和理解事件、变量或行为之间的因果关系。从大模型与因果推断相互结合的可能性和难点、大模型预训练、因果模型的人类反馈学习过程和微调技术等方面进行探讨,论述了大模型具有发现潜在因果关系的机制和因果关系预测及解释的潜力。此外,归纳了部分大模型开源工具,可用于快速实现大模型训练、验证和部署。

Large model is a machine learning model with huge parameters trained from massive data,and Causal inference is to infer and understand the causal relationship between events,variables or behaviors.It discusses the possibility and difficulty of the combination of the large model and Causal inference,the pre-training of the large model,the human feedback learning process of the causal model,and the fine-tuning technology.It shows that the large model has the mechanism of discovering potential causal relationships and the potential of causal relationship prediction and interpretation.In addition,it summarizes some open-source tools for large models,which can be used to quickly implement large model training,validation,and deployment.