Summary of A Study on Effect Of Reference Knowledge Choice in Generating Technical Content Relevant to Sapphire Model Using Large Language Model, by Kausik Bhattacharya et al.
A Study on Effect of Reference Knowledge Choice in Generating Technical Content Relevant to SAPPhIRE Model Using Large Language Model
by Kausik Bhattacharya, Anubhab Majumder, Amaresh Chakrabarti
First submitted to arxiv on: 29 Jun 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A novel method for generating accurate technical content related to the SAPPhIRE model of causality using Large Language Models (LLMs) is proposed in this research. The approach, which combines Retrieval Augmented Generation with LLMs, aims to suppress hallucinations and produce technically supported content relevant to a given SAPPhIRE construct. The study highlights the importance of selecting appropriate reference knowledge for providing context to the LLM, and demonstrates how this method can be used to build software tools for generating SAPPhIRE models of technical systems. This research has implications for applications in design and other fields where accurate representation of complex systems is crucial. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Scientists are trying to help people create better pictures of how things work using a special model called SAPPhIRE. To do this, they need to gather information from many different sources about how the thing works. This research explores how computers can be used to help with this process by generating text that is accurate and relevant to the SAPPhIRE model. The study shows that it’s important to choose the right information for the computer to use as a reference when generating the text. This research has the potential to make it easier for people to create these pictures of how things work. |
Keywords
» Artificial intelligence » Retrieval augmented generation
