Summary of Game Of Llms: Discovering Structural Constructs in Activities Using Large Language Models, by Shruthi K. Hiremath and Thomas Ploetz
Game of LLMs: Discovering Structural Constructs in Activities using Large Language Models
by Shruthi K. Hiremath, Thomas Ploetz
First submitted to arxiv on: 19 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- 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 This paper proposes a new approach for human activity recognition in smart homes by identifying underlying building blocks using large language models. The traditional method assumes a constant window length, which is not suitable for smart home scenarios where activities vary in duration and frequency. By recognizing these building blocks, the authors aim to improve the recognition of short-duration and infrequent activities. They propose a new procedure that uses these building blocks to model activities, ultimately enhancing activity monitoring in smart homes. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about using big language models to figure out how people behave at home. Right now, people are trying to recognize what people are doing by looking at small chunks of time, but this doesn’t work well for things that happen quickly or only sometimes. The authors want to find the basic building blocks of activities, like taking a shower or watching TV, and use those to understand what’s happening in smart homes. |
Keywords
» Artificial intelligence » Activity recognition
