Learning to explain is a good biomedical few-shot learner.

ABSTRACT:

Motivation

Significant progress has been achieved in biomedical text mining using deep learning methods, which rely heavily on large amounts of high-quality data annotated by human experts. However, the reality is that obtaining high-quality annotated data is extremely challenging due to data scarcity (e.g. rare or new diseases), data privacy and security concerns, and the high cost of data annotation. Additionally, nearly all researches focus on predicting labels without providing corresponding explanations. Therefore, in this paper, we investigate a more realistic scenario, biomedical few-shot learning, and explore the impact of interpretability on biomedical few-shot learning.

Results

We present LetEx-Learning to explain-a novel multi-task generative approach that leverag

SUBMITTER: Chen P 

PROVIDER: S-EPMC11483110 | biostudies-literature | 2024 Oct

REPOSITORIES: biostudies-literature