Summary of Cps-llm: Large Language Model Based Safe Usage Plan Generator For Human-in-the-loop Human-in-the-plant Cyber-physical System, by Ayan Banerjee et al.
CPS-LLM: Large Language Model based Safe Usage Plan Generator for Human-in-the-Loop Human-in-the-Plant Cyber-Physical System
by Ayan Banerjee, Aranyak Maity, Payal Kamboj, Sandeep K.S. Gupta
First submitted to arxiv on: 19 May 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Systems and Control (eess.SY)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| 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 approach in machine learning enables the integration of large language models (LLMs) with human-in-the-loop cyber-physical systems (CPSs) to generate personalized plans and grounded inferences for control goal achievement. By contextualizing LLMs, it is possible to create domain-specific plans, but these may be infeasible or unsafe for physical execution or human users. To address this, the proposed CPS-LLM retraining framework ensures generated plans align with physical system dynamics and are safe for humans. This involves two innovative components: a liquid time constant neural network-based physical dynamics coefficient estimator and an LLM trained on prompts with model coefficients. The integration of CPS-LLM with contextualized chatbots, such as BARD, enables the generation of feasible and safe plans to manage external events in applications like automated insulin delivery systems for Type 1 Diabetes subjects. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A team of researchers has developed a new way to use large language models (LLMs) to help people with diabetes. They created a special kind of LLM that can work together with computers to make decisions and plans. This LLM is called CPS-LLM, and it’s really good at coming up with ideas for managing things like meals and insulin delivery. The researchers tested their system with a chatbot called BARD and found that it could help people with diabetes take better care of themselves. |
Keywords
» Artificial intelligence » Machine learning » Neural network
