Papers by Danny Chen

2 papers
Text2Tree: Aligning Text Representation to the Label Tree Hierarchy for Imbalanced Medical Classification (2023.findings-emnlp)

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Challenge: Existing approaches to medical text classification are struggling with imbalanced data distribution and rare labels.
Approach: They propose a framework-agnostic algorithm that only utilizes internal label hierarchy in training deep learning models.
Outcome: The proposed approach performs better on public datasets and real-world medical records than existing methods.
Discovering Language Model Behaviors with Model-Written Evaluations (2023.findings-acl)

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Challenge: Prior work creates evaluations with crowdwork or existing data sources, which are not always available.
Approach: They generate evaluations automatically with language models (LMs) using crowdwork or existing data sources to find out how they behave .
Outcome: The results show that large LMs repeat back a dialog user’s preferred answer and express greater desire to pursue concerning goals like resource acquisition and goal preservation.

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