Summary of Quo Vadis Chatgpt? From Large Language Models to Large Knowledge Models, by Venkat Venkatasubramanian and Arijit Chakraborty
Quo Vadis ChatGPT? From Large Language Models to Large Knowledge Models
by Venkat Venkatasubramanian, Arijit Chakraborty
First submitted to arxiv on: 29 May 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL)
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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 The recent success of transformer-based generative neural networks in natural language processing and image synthesis has sparked excitement about potential applications in process systems engineering (PSE). Large Language Models (LLMs) have achieved impressive results in tasks like document writing, code writing assistance, and text summarization. However, their limitations are evident in highly scientific domains like chemical engineering, where they lack domain knowledge and cannot reason, plan, or explain due to fundamental laws of physics, chemistry, and biology. To overcome this limitation, we propose developing hybrid AI systems that combine first principles and technical knowledge effectively. These Large Knowledge Models (LKMs) would go beyond NLP-based techniques and applications. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large Language Models have been making waves with their ability to generate human-like text and perform tasks like writing documents and summarizing texts. However, they struggle when it comes to scientific domains like chemical engineering, where complex laws and principles govern the behavior of materials and systems. To succeed in these areas, we need AI that can reason, plan, and explain based on fundamental knowledge. That’s why we’re proposing a new type of AI called Large Knowledge Models (LKMs) that combine data-driven machine learning with first principles and technical knowledge. |
Keywords
» Artificial intelligence » Image synthesis » Machine learning » Natural language processing » Nlp » Summarization » Transformer
