mlsanityData to 13 Oct 2026

A Study on Effect of Reference Knowledge Choice in Generating Technical Content Relevant to SAPPhIRE Model Using Large Language Model

arXiv 2407.00396 ↗First posted 29 Jun 2024cs.CL cs.AIcited by 0

Not on the map. Why?

Abstract (arXiv)

Representation of systems using the SAPPhIRE model of causality can be an inspirational stimulus in design. However, creating a SAPPhIRE model of a technical or a natural system requires sourcing technical knowledge from multiple technical documents regarding how the system works. This research investigates how to generate technical content accurately relevant to the SAPPhIRE model of causality using a Large Language Model, also called LLM. This paper, which is the first part of the two-part research, presents a method for hallucination suppression using Retrieval Augmented Generating with LLM to generate technical content supported by the scientific information relevant to a SAPPhIRE con-struct. The result from this research shows that the selection of reference knowledge used in providing context to the LLM for generating the technical content is very important. The outcome of this research is used to build a software support tool to generate the SAPPhIRE model of a given technical system.

Key facts. No citing papers in the map yet (data through 13 Oct 2026). Builds on 6 of its 21 references that are in the map. First posted on arXiv 29 Jun 2024.

Builds on

6 of 21 references.

References · 6

  1. 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Taskscited by 9,221
  2. 2024A Comprehensive Survey of Hallucination Mitigation Techniques in Large Language Modelscited by 268
  3. 2023LLMs4OL: Large Language Models for Ontology Learningcited by 76
  4. 2024AutoTRIZ: Automating Engineering Innovation with TRIZ and Large Language Modelscited by 9
  5. 2023A Reliable Knowledge Processing Framework for Combustion Science using Foundation Modelscited by 7
  6. 2024Development and Evaluation of a Retrieval-Augmented Generation Tool for Creating SAPPhIRE Models of Artificial Systemscited by 3

Built on by

Not cited yet.

Similar, not cited

Close in wording, no citation either way

  1. 2025LLM Enhancement with Domain Expert Mental Model to Reduce LLM Hallucination with Causal Prompt Engineering
  2. 2023Evaluating the Effectiveness of Retrieval-Augmented Large Language Models in Scientific Document Reasoning
  3. 2025MultiRAG: A Knowledge-guided Framework for Mitigating Hallucination in Multi-source Retrieval Augmented Generation
  4. 2025Hallucinate Less by Thinking More: Aspect-Based Causal Abstention for Large Language Models
  5. 2026Retrieval-Augmented Large Language Models as Components of Cognitive Computing architecture for Regulatory Knowledge Management
  6. 2024Deploying Large Language Models With Retrieval Augmented Generation
  7. 2026RADIANT-LLM: an Agentic Retrieval Augmented Generation Framework for Reliable Decision Support in Safety-Critical Nuclear Engineering
  8. 2024Improving Scientific Hypothesis Generation with Knowledge Grounded Large Language Models