Papers by Zhiyuan Kan

2 papers
Expertise Style Transfer: A New Task Towards Better Communication between Experts and Laymen (2020.acl-main)

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Challenge: a curse of knowledge can impede communication between experts and laymen.
Approach: They propose a task of expertise style transfer to automate the communication between experts and laymen using an annotated dataset.
Outcome: The proposed task simplifies professional language and improves accuracy and expertise level of laymen descriptions using simple words.
Large Language Models Are Still Misled by Simple Bias Ensembles (2026.findings-acl)

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Challenge: Existing benchmarks for large language models are constrained to datasets where each sample is manually injected with only one type of bias.
Approach: They propose a multi-bias benchmark where each sample contains multiple types of biases.
Outcome: The proposed benchmark shows that existing LLMs and debiasing methods perform poorly on this benchmark, highlighting the challenge of eliminating compounded biases.

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