FoodGPT: A Large Language Model in Food Testing Domain with Incremental Pre-training and Knowledge Graph Prompt

• URL: http://arxiv.org/abs/2308.10173v1
• Date: Sun, 20 Aug 2023 05:58:33 GMT
• Title: FoodGPT: A Large Language Model in Food Testing Domain with Incremental Pre-training and Knowledge Graph Prompt
• Authors: Zhixiao Qi, Yijiong Yu, Meiqi Tu, Junyi Tan, Yongfeng Huang
• Abstract summary: We build a large language model for food testing. In this paper, we propose a method for handling structured knowledge and scanned documents in incremental pre-training.
• Score: 18.7168443402118
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Currently, the construction of large language models in specific domains is done by fine-tuning on a base model. Some models also incorporate knowledge bases without the need for pre-training. This is because the base model already contains domain-specific knowledge during the pre-training process. We build a large language model for food testing. Unlike the above approach, a significant amount of data in this domain exists in Scanning format for domain standard documents. In addition, there is a large amount of untrained structured knowledge. Therefore, we introduce an incremental pre-training step to inject this knowledge into a large language model. In this paper, we propose a method for handling structured knowledge and scanned documents in incremental pre-training. To overcome the problem of machine hallucination, we constructe a knowledge graph to serve as an external knowledge base for supporting retrieval in the large language model. It is worth mentioning that this paper is a technical report of our pre-release version, and we will report our specific experimental data in future versions.

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