most citedGender Bias in Large Language Models across Multiple Languages

9 citations · 11 across the 5 of their papers we have counts for

collaborators

5 papers

cs.CL2024

Can Language Model Understand Word Semantics as A Chatbot? An Empirical Study of Language Model Internal External Mismatch

Jinman Zhao, Xueyan Zhang, Xingyu Yue +3

Current common interactions with language models is through full inference. This approach may not necessarily align with the model's internal knowledge. Studies show discrepancies…

cs.CL2024

LongRecipe: Recipe for Efficient Long Context Generalization in Large Language Models

Zhiyuan Hu, Yuliang Liu, Jinman Zhao +8

Large language models (LLMs) face significant challenges in handling long-context tasks because of their limited effective context window size during pretraining, which restricts t…

cs.IR2024

Data Imputation using Large Language Model to Accelerate Recommendation System

Zhicheng Ding, Jiahao Tian, Zhenkai Wang +2

This paper aims to address the challenge of sparse and missing data in recommendation systems, a significant hurdle in the age of big data. Traditional imputation methods struggle…

cs.CL20249 cited

Gender Bias in Large Language Models across Multiple Languages

Jinman Zhao, Yitian Ding, Chen Jia +2

With the growing deployment of large language models (LLMs) across various applications, assessing the influence of gender biases embedded in LLMs becomes crucial. The topic of gen…

cs.SE20232 cited

Better Context Makes Better Code Language Models: A Case Study on Function Call Argument Completion

Hengzhi Pei, Jinman Zhao, Leonard Lausen +2

Pretrained code language models have enabled great progress towards program synthesis. However, common approaches only consider in-file local context and thus miss information and…