mlsanityData to 7 Oct 2026

Opportunities for Large Language Models and Discourse in Engineering Design

arXiv 2306.09169 ↗First posted 15 Jun 2023cs.CL cs.AI cs.CEcited by 6

Not on the map. Why?

Abstract (arXiv)

In recent years, large language models have achieved breakthroughs on a wide range of benchmarks in natural language processing and continue to increase in performance. Recently, the advances of large language models have raised interest outside the natural language processing community and could have a large impact on daily life. In this paper, we pose the question: How will large language models and other foundation models shape the future product development process? We provide the reader with an overview of the subject by summarizing both recent advances in natural language processing and the use of information technology in the engineering design process. We argue that discourse should be regarded as the core of engineering design processes, and therefore should be represented in a digital artifact. On this basis, we describe how foundation models such as large language models could contribute to the design discourse by automating parts thereof that involve creativity and reasoning, and were previously reserved for humans. We describe how simulations, experiments, topology optimizations, and other process steps can be integrated into a machine-actionable, discourse-centric design process. Finally, we outline the future research that will be necessary for the implementation of the conceptualized framework.

Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). Builds on 41 of its 62 references that are in the map. First posted on arXiv 15 Jun 2023.

Builds on

41 of 62 references.

References · 41

  1. 2017Attention Is All You Needcited by 59,932
  2. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
  3. 2020Language Models are Few-Shot Learnerscited by 32,199
  4. 2022Training language models to follow instructions with human feedbackcited by 15,712
  5. 2019Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformercited by 14,491
  6. 2023LLaMA: Open and Efficient Foundation Language Modelscited by 13,958
  7. 2019RoBERTa: A Robustly Optimized BERT Pretraining Approachcited by 13,266
  8. 2022Chain-of-Thought Prompting Elicits Reasoning in Large Language Modelscited by 12,302
  9. 2022ReAct: Synergizing Reasoning and Acting in Language Modelscited by 8,355
  10. 2013Efficient Estimation of Word Representations in Vector Spacecited by 4,346
  11. 2023Toolformer: Language Models Can Teach Themselves to Use Toolscited by 4,072
  12. 2021On the Opportunities and Risks of Foundation Modelscited by 3,961
29 more
  1. 2013Distributed Representations of Words and Phrases and their Compositionalitycited by 3,462
  2. 2022Scaling Instruction-Finetuned Language Modelscited by 2,997
  3. 2022Emergent Abilities of Large Language Modelscited by 2,483
  4. 2018Deep contextualized word representationscited by 2,285
  5. 2023PaLM-E: An Embodied Multimodal Language Modelcited by 2,037
  6. 2022BLOOM: A 176B-Parameter Open-Access Multilingual Language Modelcited by 1,968
  7. 2019SuperGLUE: A Stickier Benchmark for General-Purpose Language Understanding Systemscited by 1,718
  8. 2021Show Your Work: Scratchpads for Intermediate Computation with Language Modelscited by 871
  9. 2021Probing Classifiers: Promises, Shortcomings, and Advancescited by 831
  10. 2023Are Emergent Abilities of Large Language Models a Mirage?cited by 515
  11. 2022Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Taskcited by 417
  12. 2022UL2: Unifying Language Learning Paradigmscited by 292
  13. 2022Faithful Reasoning Using Large Language Modelscited by 130
  14. 2022Vision-Language Pre-training: Basics, Recent Advances, and Future Trendscited by 117
  15. 2021Deep Generative Models in Engineering Design: A Reviewcited by 103
  16. 2020Stopwords in Technical Language Processingcited by 29
  17. 2022Analogy Generation by Prompting Large Language Models: A Case Study of InstructGPTcited by 27
  18. 2023Multi-modal Machine Learning in Engineering Design: A Review and Future Directionscited by 27
  19. 2023Fluid Transformers and Creative Analogies: Exploring Large Language Models' Capacity for Augmenting Cross-Domain Analogical Creativitycited by 23
  20. 2023Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Designcited by 22
  21. 2022Generative Transformers for Design Concept Generationcited by 21
  22. 2021Engineering Knowledge Graph from Patent Databasecited by 18
  23. 2021Generative Pre-Trained Transformer for Design Concept Generation: An Explorationcited by 17
  24. 2021Natural Language Processing in-and-for Design Researchcited by 15
  25. 2019TechNet: Technology Semantic Network Based on Patent Datacited by 14
  26. 2022Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformerscited by 13
  27. 2023Conceptual Design Generation Using Large Language Modelscited by 13
  28. 2020Semantic Networks for Engineering Design: A Surveycited by 6
  29. 2021Design Strategy Network: A deep hierarchical framework to represent generative design strategies in complex action spacescited by 4

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 1; 2025: 1; 2026: 4.

6 papers.