output
20062026
most citedJobFair: A Framework for Benchmarking Gender Hiring Bias in Large Language Models

9 citations

researchers with a paper here

Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.

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Showing cs.CLShow all

2 papers · 1 filter

cs.CL2026

Tool Calling is Linearly Readable and Steerable in Language Models

Zekun Wu, Ze Wang, Seonglae Cho +4

Language-model agents can take real actions by calling tools, so choosing the wrong tool can cause errors that are difficult to undo. Most evaluations only observe the tool choice…

cs.CL2024★ 9 cited

JobFair: A Framework for Benchmarking Gender Hiring Bias in Large Language Models

Ze Wang, Zekun Wu, Xin Guan +6

The use of Large Language Models (LLMs) in hiring has led to legislative actions to protect vulnerable demographic groups. This paper presents a novel framework for benchmarking hi…