mlsanityData to 14 Oct 2026

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

arXiv 2308.10173 ↗First posted 20 Aug 2023cs.CLcited by 14

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Abstract (arXiv)

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.

Key facts. Cited by 14 papers in the map (data through 14 Oct 2026). Builds on 10 of its 13 references that are in the map. First posted on arXiv 20 Aug 2023.

Builds on

10 of 13 references.

References · 10

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
  2. 2020Language Models are Few-Shot Learnerscited by 32,419
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  10. 2023Chatlaw: A Multi-Agent Legal Assistant based on a Role-Aligned Mixture-of-Experts Architecturecited by 122

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Citing papers per year: 2023: 3; 2024: 6; 2025: 5.

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  1. 2023A Survey on Hallucination in Large Language Models: Principles, Taxonomy, Challenges, and Open Questionscited by 1,601
  2. 2024Graph Retrieval-Augmented Generation: A Surveycited by 324
  3. 2023MetaTool Benchmark for Large Language Models: Deciding Whether to Use Tools and Which to Usecited by 217
  4. 2023Survey on Factuality in Large Language Models: Knowledge, Retrieval and Domain-Specificitycited by 206
  5. 2024Retrieval-Augmented Generation with Knowledge Graphs for Customer Service Question Answeringcited by 96
  6. 2025Graph-based Approaches and Functionalities in Retrieval-Augmented Generation: A Comprehensive Surveycited by 18
  7. 2024ChIRAAG: ChatGPT Informed Rapid and Automated Assertion Generationcited by 16
  8. 2024FoodSky: A Food-oriented Large Language Model that Passes the Chef and Dietetic Examinationcited by 10
  9. 2025Are Vision-Language Models Ready for Dietary Assessment? Exploring the Next Frontier in AI-Powered Food Image Recognitioncited by 7
  10. 2024WeKnow-RAG: An Adaptive Approach for Retrieval-Augmented Generation Integrating Web Search and Knowledge Graphscited by 6
  11. 2025Large Language Models for Knowledge Graph Embedding: A Surveycited by 3
  12. 2025KERL: Knowledge-Enhanced Personalized Recipe Recommendation using Large Language Modelscited by 2
2 more
  1. 2024Extracting chemical food safety hazards from the scientific literature automatically using large language modelscited by 1
  2. 2025Enhancing Retrieval-Augmented Generation for Electric Power Industry Customer Supportcited by 0