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Found 7 papers, 4 papers with code
Large Language Models in Mental Health Care: a Scoping Review
Objective: The growing use of large language models (LLMs) stimulates a need for a comprehensive review of their applications and outcomes in mental health care contexts.
Labrador: Exploring the Limits of Masked Language Modeling for Laboratory Data
In this work we introduce Labrador, a pre-trained Transformer model for laboratory data.
Conformal Prediction with Large Language Models for Multi-Choice Question Answering
In this work, we explore how conformal prediction can be used to provide uncertainty quantification in language models for the specific task of multiple-choice question-answering.
Towards Reliable Zero Shot Classification in Self-Supervised Models with Conformal Prediction
Self-supervised models trained with a contrastive loss such as CLIP have shown to be very powerful in zero-shot classification settings.
Coarse-to-Fine Memory Matching for Joint Retrieval and Classification
We present a novel end-to-end language model for joint retrieval and classification, unifying the strengths of bi- and cross- encoders into a single language model via a coarse-to-fine memory matching search procedure for learning and inference.
Empirical Frequentist Coverage of Deep Learning Uncertainty Quantification Procedures
Uncertainty quantification for complex deep learning models is increasingly important as these techniques see growing use in high-stakes, real-world settings.
Exemplar Auditing for Multi-Label Biomedical Text Classification
These challenges are compounded for modalities such as text, where the feature space is very high-dimensional, and often contains considerable amounts of noise.
