An LLM-augmented synthetic data pipeline produces the largest public eviction-focused SDoH dataset (14 categories) and fine-tuned open LLMs that outperform prompt-optimized GPT-4o on the authors' test sets.
PromDA: Prompt-based Data Augmentation for Low-Resource NLU Tasks
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abstract
This paper focuses on the Data Augmentation for low-resource Natural Language Understanding (NLU) tasks. We propose Prompt-based D}ata Augmentation model (PromDA) which only trains small-scale Soft Prompt (i.e., a set of trainable vectors) in the frozen Pre-trained Language Models (PLMs). This avoids human effort in collecting unlabeled in-domain data and maintains the quality of generated synthetic data. In addition, PromDA generates synthetic data via two different views and filters out the low-quality data using NLU models. Experiments on four benchmarks show that synthetic data produced by PromDA successfully boost up the performance of NLU models which consistently outperform several competitive baseline models, including a state-of-the-art semi-supervised model using unlabeled in-domain data. The synthetic data from PromDA are also complementary with unlabeled in-domain data. The NLU models can be further improved when they are combined for training.
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cs.CL 1years
2025 1verdicts
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SynthEHR-Eviction: Enhancing Eviction SDoH Detection with LLM-Augmented Synthetic EHR Data
An LLM-augmented synthetic data pipeline produces the largest public eviction-focused SDoH dataset (14 categories) and fine-tuned open LLMs that outperform prompt-optimized GPT-4o on the authors' test sets.