Summary of Utilizing Large Language Models to Generate Synthetic Data to Increase the Performance Of Bert-based Neural Networks, by Chancellor R. Woolsey et al.
Utilizing Large Language Models to Generate Synthetic Data to Increase the Performance of BERT-Based Neural Networks
by Chancellor R. Woolsey, Prakash Bisht, Joshua Rothman, Gondy Leroy
First submitted to arxiv on: 8 May 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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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 explores the potential of machine learning (ML) models in diagnosing patients, particularly those with Autism Spectrum Disorders (ASD). However, creating large datasets to train these models is expensive and challenging. The authors evaluated large language models (LLMs), such as ChatGPT and GPT-Premium, for data creation purposes. They prompted the LLMs to generate synthetic observations, which were then used to augment existing medical data. The goal was to label behaviors corresponding to autism criteria and improve model accuracy with synthetic training data. A BERT classifier pre-trained on biomedical literature was used to assess differences in performance between models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how machine learning can help doctors diagnose patients, especially those with autism. Right now, creating the big datasets needed to train these models is expensive and hard. The researchers tested special computer programs called large language models (LLMs) to see if they could make fake data that helps train the models better. They used LLMs like ChatGPT and GPT-Premium to generate lots of synthetic observations, which were then added to existing medical data. Their goal was to improve model accuracy by labeling behaviors that match autism criteria. The results show that using this fake data can actually make the models a little more accurate, but also a little less precise. |
Keywords
» Artificial intelligence » Bert » Gpt » Machine learning
