activity
20232026
most citedUnmasking and Quantifying Racial Bias of Large Language Models in Medical Report Generation

89 citations · 107 across the 5 of their papers we have counts for

collaborators

5 papers

cs.IR2026

Beyond Matching: Category-Guided Latent Intent Reasoning for Generative Retrieval in E-Commerce

Fuwei Zhang, Xiaoyu Liu, Jiajie Jin +8

Generative retrieval offers a new paradigm for e-commerce search by mapping user queries directly to product Semantic Identifiers (SIDs). However, e-commerce queries are often shor…

cs.CL2024★ 3 cited

Ensuring Safety and Trust: Analyzing the Risks of Large Language Models in Medicine

Yifan Yang, Qiao Jin, Robert Leaman +15

The remarkable capabilities of Large Language Models (LLMs) make them increasingly compelling for adoption in real-world healthcare applications. However, the risks associated with…

cs.CL2024★ 14 cited

Large Language Models and Causal Inference in Collaboration: A Survey

Xiaoyu Liu, Paiheng Xu, Junda Wu +10

Causal inference has shown potential in enhancing the predictive accuracy, fairness, robustness, and explainability of Natural Language Processing (NLP) models by capturing causal…

cs.CL2024★ 89 cited

Unmasking and Quantifying Racial Bias of Large Language Models in Medical Report Generation

Yifan Yang, Xiaoyu Liu, Qiao Jin +2

Large language models like GPT-3.5-turbo and GPT-4 hold promise for healthcare professionals, but they may inadvertently inherit biases during their training, potentially affecting…

cs.LG2023★ 1 cited

C-Disentanglement: Discovering Causally-Independent Generative Factors under an Inductive Bias of Confounder

Xiaoyu Liu, Jiaxin Yuan, Bang An +3

Representation learning assumes that real-world data is generated by a few semantically meaningful generative factors (i.e., sources of variation) and aims to discover them in the…