Leveraging Large Language Models for Improved Health-Related Text Classification on Social Media Data
核心概念
Employing data augmentation using large language models (GPT-4) with human-annotated data to train lightweight supervised classification models achieves superior results compared to training with human-annotated data alone or using large language models as zero-shot classifiers.
摘要
The study evaluates the performance of various text classification approaches, including supervised classic machine learning models, supervised pre-trained language models, and large language models (LLMs), on six health-related text classification tasks using social media data.
The key findings are:
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Using LLM-annotated data alone for training supervised classification models is ineffective, as the supervised models trained on human-annotated data outperform those trained on LLM-annotated data.
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LLMs, when used as zero-shot classifiers, show promise in excluding false negatives and potentially reducing the human effort required for data annotation. The GPT-4 zero-shot classifier achieved higher recall than the supervised models in most tasks.
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The most optimal strategy is to leverage data augmentation using LLMs (GPT-4) with human-annotated data to train lightweight supervised classification models, which achieves superior results compared to training with human-annotated data alone or using LLMs as zero-shot classifiers.
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The effectiveness of data augmentation using LLMs is task-specific, and the optimal number of augmented data and the ideal ratio of human-annotated data to LLM-augmented data require further investigation.
Evaluating Large Language Models for Health-Related Text Classification Tasks with Public Social Media Data
統計資料
"SSRIs have never done much for me' I've been medication-free for 3 years. I cope well at times, am dysfunctional other times. I hope to be more consistently regulated. I also hope to have fewer intrusive thoughts/memories, and reduce anxiety and depression symptoms."
"Three years off meds and still fighting. Some days are easier than others. H're's to hoping for a future with less anxiety, fewer intrusive thoughts, and a mind free from recurring distressing memories."
引述
"By leveraging this data augmentation strategy, we can harness the power of LLMs to develop smaller, more effective domain-specific NLP models."
"LLM-annotated data without human guidance for training lightweight supervised classification models is an ineffective strategy."
"LLM, as a zero-shot classifier, shows promise in excluding false negatives and potentially reducing the human effort required for data annotation."
深入探究
Potential Biases and Limitations of Using LLMs for Data Augmentation in Health-Related Text Classification Tasks and Mitigation Strategies
Using LLMs for data augmentation in health-related text classification tasks can introduce biases and limitations. One potential bias is the generation of synthetic data that may not accurately represent the diversity and complexity of real-world health-related text. This can lead to model overfitting and reduced generalizability. To mitigate this, researchers can implement diversity constraints during data augmentation to ensure that the generated data covers a wide range of scenarios and variations present in the original dataset.
Another limitation is the potential introduction of noise or incorrect information by LLMs during data augmentation. This can impact the overall performance of the classification model. To address this, researchers can implement quality control measures such as post-generation filtering or human review of augmented data to ensure its accuracy and relevance to the task at hand.
Improving the Performance of LLMs as Zero-Shot Classifiers and Implications for Reducing Manual Data Annotation
To enhance the performance of LLMs as zero-shot classifiers, researchers can focus on optimizing prompt engineering techniques. By designing effective prompts that guide the LLMs to make accurate predictions, the model's performance can be improved significantly. Additionally, fine-tuning LLMs on domain-specific data or incorporating domain knowledge into the prompts can further enhance their zero-shot classification capabilities.
Reducing the need for manual data annotation through the use of LLMs as zero-shot classifiers has significant implications for streamlining the text classification process. By leveraging the strengths of LLMs in understanding and generating text, researchers can expedite the model development process and reduce the human effort required for data annotation. This can lead to faster model deployment and scalability in handling large volumes of text data in various domains beyond healthcare.
Extending Findings to Other Domains and Broader Implications for LLMs in Text Classification Tasks
The findings from this study can be extended to other domains beyond healthcare by adapting the strategies developed for leveraging LLMs in text classification tasks. Researchers in fields such as finance, marketing, or legal can explore similar approaches to enhance their text classification models using LLMs for data augmentation, zero-shot classification, and supervised learning.
The broader implications of using LLMs in text classification tasks lie in the potential for developing more efficient and accurate NLP models across various domains. By harnessing the power of LLMs for data augmentation, researchers can improve model performance with limited annotated data. This can lead to the development of more robust and domain-specific NLP models that can handle diverse text data with high accuracy and efficiency.
