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Found 6 papers, 3 papers with code
Utilizing Large Language Models to Generate Synthetic Data to Increase the Performance of BERT-Based Neural Networks
Augmenting data increased recall by 13% but decreased precision by 16%, correlating with higher quality and lower accuracy across pairs.
Text and Audio Simplification: Human vs. ChatGPT
We scored twelve corpora using our metrics: six text, one audio, and five ChatGPT simplified corpora.
Effects of Added Emphasis and Pause in Audio Delivery of Health Information
In this study, we evaluate the effect of audio enhancements in the form of information emphasis and pauses with health texts of varying difficulty and we measure health information comprehension and retention.
APPLS: Evaluating Evaluation Metrics for Plain Language Summarization
In response, we introduce POMME, a new metric designed to assess text simplification in PLS; the metric is calculated as the normalized perplexity difference between an in-domain and out-of-domain language model.
Retrieval augmentation of large language models for lay language generation
Recent lay language generation systems have used Transformer models trained on a parallel corpus to increase health information accessibility.
AutoMeTS: The Autocomplete for Medical Text Simplification
In this paper, we examine the application of autocomplete to text simplification in the medical domain.
