Enhancing Detection of Message Intents in a Mobile Health Smoking-Cessation Intervention Using Large Language Model Fine-Tuning, Data Downsampling, and Error Correction: Algorithm Development and Validation

TL;DR

This paper develops an AI chatbot for smoking cessation by improving intent detection using large language models, data balancing, and error correction.

Contribution

A novel AI method combining fine-tuning, downsampling, and error correction to enhance intent detection in mobile health interventions.

Findings

Fine-tuning improved F1-scores from 0.38 to 0.80 on a downsampled dataset.

Downsampling and error correction together achieved 0.86 unweighted F1-score.

Automated downsampling alone may be sufficient for performance improvement.

Abstract

Although smoking-cessation aids such as support groups and nicotine replacement therapy (NRT) can help people quit, quit rates remain low. Mobile health interventions can boost accessibility and engagement, especially with NRT, but require ongoing effort to deliver timely responses. Accurate intent detection is crucial for identifying user needs and delivering timely, appropriate chatbot responses. Recent large language model advancements in natural language processing and artificial intelligence (AI) have shown promise. However, these systems often struggle with many intent categories, complex language, and imbalanced data, reducing recognition accuracy.

The main goal of this study was to develop an AI tool, a large language model that could accurately detect people’s message intents, despite dataset imbalances and complexities. In our application, the messages came from a smoking-cessation support-group intervention and often involved the use of NRT provided as part of that intervention.

We consistently used a state-of-the-art public domain large language model, Llama-3 8B (8 billion parameters) from Meta. First, we used the model off-the-shelf. Second, we fine-tuned it on our annotated dataset with 25 intent categories. Third, we also downsampled the predominant intent category to reduce model bias. Finally, we combined downsampling with corrected human annotations, creating a cleaned dataset for a new round of fine-tuning.

Without fine-tuning, the model achieved unweighted and weighted F1-scores (overall performance) of 0.41 and 0.38, respectively, on the downsampled corrected test dataset, and 0.29 and 0.35 on the full test dataset. Fine-tuning improved performance to 0.77 and 0.80 on the downsampled corrected dataset, and 0.72 and 0.86 on the full dataset. Fine-tuning with downsampling attained the best F1-scores, 0.88 and 0.91 on the downsampled corrected dataset, though performance dropped on the full test dataset (0.58 unweighted, 0.66 weighted) due to the predominance of the off-topic intent category, while unweighted recall remained high (0.80). The final method combining fine-tuning, downsampling, and error correction achieved 0.86 unweighted and 0.90 weighted F1-scores on the downsampled corrected dataset, and 0.57 and 0.65 on the full dataset with unweighted recall improving to 0.82.

Large language models performed poorly without fine-tuning, highlighting the need for domain-specific training. Even with fine-tuning, performance was limited by a highly imbalanced dataset. Downsampling before fine-tuning moderately improved performance but still left room for improvement and concerns about dataset noise. A careful review of model-human disagreement cases helped identify human annotation errors. After error correction, the method without error correction still achieved slightly higher precision and F1-score on the corrected test dataset. While error correction slightly improved recall on noisy data, automated downsampling alone may be sufficient, making manual correction a more resource-intensive option with limited added benefit.

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