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

arXiv: https://arxiv.org/abs/2407.00396 · HTML: https://mlsanity.com/paper/2407.00396/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 21 references that are in the map.
- First posted on arXiv 29 Jun 2024.

## Builds on (most cited first, 6 of 6)

- [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://mlsanity.com/paper/2005.11401/) (2020)
- [A Comprehensive Survey of Hallucination Mitigation Techniques in Large Language Models](https://mlsanity.com/paper/2401.01313/) (2024)
- [LLMs4OL: Large Language Models for Ontology Learning](https://mlsanity.com/paper/2307.16648/) (2023)
- [AutoTRIZ: Automating Engineering Innovation with TRIZ and Large Language Models](https://mlsanity.com/paper/2403.13002/) (2024)
- [A Reliable Knowledge Processing Framework for Combustion Science using Foundation Models](https://mlsanity.com/paper/2401.00544/) (2023)
- [Development and Evaluation of a Retrieval-Augmented Generation Tool for Creating SAPPhIRE Models of Artificial Systems](https://mlsanity.com/paper/2406.19493/) (2024)

## 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.