Summary of Large Language Models Are Zero-shot Recognizers For Activities Of Daily Living, by Gabriele Civitarese et al.
Large Language Models are Zero-Shot Recognizers for Activities of Daily Living
by Gabriele Civitarese, Michele Fiori, Priyankar Choudhary, Claudio Bettini
First submitted to arxiv on: 1 Jul 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL); Signal Processing (eess.SP)
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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 proposed ADL-LLM system utilizes Large Language Models (LLMs) to recognize Activities of Daily Living (ADLs) in smart home environments. By transforming raw sensor data into textual representations and processing them with an LLM, the system achieves zero-shot ADLs recognition. Additionally, when a small labeled dataset is available, the system can be empowered with few-shot prompting. The effectiveness of ADL-LLM was evaluated on two public datasets, demonstrating its ability to recognize ADLs in this domain. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary ADL-LLM is a new way to recognize daily activities using smart home sensors and big language models. It takes sensor data and turns it into text that the model can understand. This helps it recognize what people are doing without needing lots of training data. The system also works when there’s some labeled data available, making it more accurate. Tests on two public datasets showed that ADL-LLM is good at recognizing daily activities. |
Keywords
» Artificial intelligence » Few shot » Prompting » Zero shot
