Summary of Domain-specific React For Physics-integrated Iterative Modeling: a Case Study Of Llm Agents For Gas Path Analysis Of Gas Turbines, by Tao Song and Yuwei Fan and Chenlong Feng and Keyu Song and Chao Liu and Dongxiang Jiang
Domain-specific ReAct for physics-integrated iterative modeling: A case study of LLM agents for gas path analysis of gas turbines
by Tao Song, Yuwei Fan, Chenlong Feng, Keyu Song, Chao Liu, Dongxiang Jiang
First submitted to arxiv on: 1 Jun 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computational Engineering, Finance, and Science (cs.CE); Machine Learning (cs.LG)
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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 study applies large language models (LLMs) with callable tools in energy and power engineering, specifically focusing on gas path analysis of gas turbines. A dual-agent tool-calling process integrates expert knowledge, predefined tools, and LLM reasoning. Various LLMs were evaluated, including LLama3, Qwen1.5, and GPT, showing smaller models struggled with tool usage and parameter extraction, while larger models demonstrated favorable capabilities. However, all models faced challenges with complex, multi-component problems. The study infers that LLMs with nearly 100 billion parameters can meet professional scenario requirements with fine-tuning and advanced prompt design, paving the way for more robust AI-driven solutions. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large language models are being used to help experts in energy and power engineering. These models are trained on lots of text data and can understand and generate human-like language. In this study, they were used to analyze gas turbines. The researchers tested different types of large language models and found that the smaller ones had trouble using tools and extracting important information from text. The bigger models did better, but still struggled with very complex problems. The study suggests that even larger models need fine-tuning and special instructions to work well in this area. |
Keywords
» Artificial intelligence » Fine tuning » Gpt » Prompt
