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Found 12 papers, 2 papers with code
The Impact of De-identification on Downstream Named Entity Recognition in Clinical Text
The impact of de-identification on data quality and, in particular, utility for developing models for downstream tasks has been more thoroughly studied for structured data than for unstructured text.
Developing a Clinical Language Model for Swedish: Continued Pretraining of Generic BERT with In-Domain Data
The use of pretrained language models, fine-tuned to perform a specific downstream task, has become widespread in NLP.
Evaluating Pretraining Strategies for Clinical BERT Models
Here, an empirical investigation is carried out in which various strategies for adapting a generic language model to the clinical domain are compared to pretraining a pure clinical language model.
Downstream Task Performance of BERT Models Pre-Trained Using Automatically De-Identified Clinical Data
The impact of the de-identification techniques is assessed by training and evaluating the models using six clinical downstream tasks.
SemEval-2025 Task 9: The Food Hazard Detection Challenge
In this challenge, we explored text-based food hazard prediction with long tail distributed classes.
Data-Constrained Synthesis of Training Data for De-Identification
In this study, we domain-adapt LLMs to the clinical domain and generate synthetic clinical texts that are machine-annotated with tags for personally identifiable information using capable encoder-based NER models.
Evaluating the Reliability of Self-Explanations in Large Language Models
This paper investigates the reliability of explanations generated by large language models (LLMs) when prompted to explain their previous output.
CICLe: Conformal In-Context Learning for Largescale Multi-Class Food Risk Classification
Contaminated or adulterated food poses a substantial risk to human health.
