Prompt-based mental health screening from social media text
This article presents a method for prompt-based mental health screening from a large and noisy dataset of social media text. Our method uses GPT 3.5. prompting to distinguish publications that may be more relevant to the task, and then uses a straightforward bag-of-words text classifier to predict actual user labels. Results are found to be on pair with a BERT mixture of experts classifier, and incurring only a fraction of its training costs.
PDF AbstractTasks
Datasets
Add Datasets
introduced or used in this paper
Results from the Paper
Submit
results from this paper
to get state-of-the-art GitHub badges and help the
community compare results to other papers.
Methods
Adam •
Attention Dropout •
BERT •
BPE •
Cosine Annealing •
Dense Connections •
Discriminative Fine-Tuning •
Dropout •
GELU •
GPT •
Layer Normalization •
Linear Layer •
Linear Warmup With Cosine Annealing •
Linear Warmup With Linear Decay •
Multi-Head Attention •
Residual Connection •
Scaled Dot-Product Attention •
Softmax •
Weight Decay •
WordPiece
