mlsanityData to 14 Oct 2026

Development and Evaluation of a Retrieval-Augmented Generation Tool for Creating SAPPhIRE Models of Artificial Systems

arXiv 2406.19493 ↗First posted 27 Jun 2024cs.CL cs.AIcited by 3

On the map: Retrieval-augmented generation → RAG pipeline design and evaluation

Abstract (arXiv)

Representing systems using the SAPPhIRE causality model is found useful in supporting design-by-analogy. However, creating a SAPPhIRE model of artificial or biological systems is an effort-intensive process that requires human experts to source technical knowledge from multiple technical documents regarding how the system works. This research investigates how to leverage Large Language Models (LLMs) in creating structured descriptions of systems using the SAPPhIRE model of causality. This paper, the second part of the two-part research, presents a new Retrieval-Augmented Generation (RAG) tool for generating information related to SAPPhIRE constructs of artificial systems and reports the results from a preliminary evaluation of the tool's success - focusing on the factual accuracy and reliability of outcomes.

Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Placed in RAG pipeline design and evaluation, one of 15 directions in Retrieval-augmented generation. Builds on 5 of its 19 references that are in the map. First posted on arXiv 27 Jun 2024.

Builds on

5 of 19 references.

Closest ancestors · 1

Same direction, newest first

  1. 2023Retrieval-Augmented Generation for Large Language Models: A Survey18 Dec

Other references · 4

  1. 2022Chain-of-Thought Prompting Elicits Reasoning in Large Language Modelscited by 12,459
  2. 2023GPQA: A Graduate-Level Google-Proof Q&A Benchmarkcited by 2,879
  3. 2022Precise Zero-Shot Dense Retrieval without Relevance Labelscited by 517
  4. 2021Data-Driven Design-by-Analogy: State of the Art and Future Directionscited by 18

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 2; 2025: 0; 2026: 1.

3 papers.

  1. 2024A Study on Effect of Reference Knowledge Choice in Generating Technical Content Relevant to SAPPhIRE Model Using Large Language Modelcited by 0
  2. 2024Assessing the Variety of a Concept Space Using an Unbiased Estimate of Rao's Quadratic Indexcited by 0
  3. 2026Mapping the RAG Landscape: A Four Axis Taxonomy of Efficiency, Defense, Interactivity, and Reasoningcited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2024Deploying Large Language Models With Retrieval Augmented Generation
  2. 2024A Survey on RAG Meeting LLMs: Towards Retrieval-Augmented Large Language Models
  3. 2025Towards End-to-End Model-Agnostic Explanations for RAG Systems
  4. 2026SoK: Agentic Retrieval-Augmented Generation (RAG): Taxonomy, Architectures, Evaluation, and Research Directions
  5. 2026Is Agentic RAG worth it? An experimental comparison of RAG approaches
  6. 2025NANOGPT: A Query-Driven Large Language Model Retrieval-Augmented Generation System for Nanotechnology Research
  7. 2025Experiments with Large Language Models on Retrieval-Augmented Generation for Closed-Source Simulation Software
  8. 2025SimulRAG: Simulator-based RAG for Grounding LLMs in Long-form Scientific QA