Summary of Evaluating Large Language Models As Virtual Annotators For Time-series Physical Sensing Data, by Aritra Hota et al.
Evaluating Large Language Models as Virtual Annotators for Time-series Physical Sensing Data
by Aritra Hota, Soumyajit Chatterjee, Sandip Chakraborty
First submitted to arxiv on: 2 Mar 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: 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 Recent advancements in large language models (LLMs) have sparked interest in exploring their potential as virtual annotators for labeling time-series physical sensing data. The traditional human-in-the-loop-based annotation approach relies on access to alternate modalities like video or audio, but this comes with concerns regarding cost, efficiency, storage, and scalability. By leveraging LLMs’ ability to comprehend vast amounts of publicly available alphanumeric data, it may be possible to utilize them as virtual annotators for raw sensor data. This paper assesses the effectiveness of state-of-the-art (SOTA) LLMs in this role, focusing on challenges faced by an LLM like GPT-4 when processing raw sensor data and the potential benefits of encoding the data using SOTA self-supervised learning (SSL) approaches. The study demonstrates that SSL-based encoding and metric-based guidance enable the LLM to provide accurate annotations without requiring fine-tuning or prompt engineering, as validated through evaluations on four benchmark HAR datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine a way to label data from sensors like accelerometers without needing extra information like videos. This is usually done by humans who have access to this extra info. However, this method has many problems, such as being expensive and taking up too much storage space. Researchers are now exploring the possibility of using special computers called large language models (LLMs) to do this job instead. These LLMs can process lots of text data and might be able to understand sensor data too. In this study, scientists tested if these LLMs could really work as virtual annotators for sensor data. They found that by using a certain way of processing the data called self-supervised learning (SSL), the LLMs could provide accurate labels without needing human help. |
Keywords
* Artificial intelligence * Fine tuning * Gpt * Prompt * Self supervised * Time series
