Abstract: The development of legal artificial intelligence algorithm models has gone through the "reasoning period" of summarizing reasoning rules,the "knowledge period" of building expert knowledge,and the "learning period" of applying machine learning,and has now entered the "big model period" of building big legal models.The construction of a legal big model is generally based on a basic big model.The main construction modes include the general large model fine-tuning mode and the expert knowledge base enhancement mode.There are significant differences between the two construction modes in terms of data preparation,computing resources,and training process.At present,relevant research at home and abroad has been invested in the exploration and practice of building big models in the legal vertical field.However,due to limitations such as low data quality,incomplete legal knowledge,difficulty in algorithm interpretation,and lack of prompt engineering,the research and application of legal big models have not yet achieved the expected results.When constructing a legal big model,we should fully consider factors such as the model's open source level,the number of training parameters,domain relevance,service model,and application scenarios to select a suitable general big model as the model base,and then improve the model effect according to four key steps:legal data,legal knowledge,instruction engineering,and result evaluation.In the application practice of several core business scenarios,we should further summarize and feedback the problems existing in the legal big model,and continuously optimize and adjust it.

Key words: artificial intelligence, legal artificial intelligence, legal big model, general large model, data