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20242026
most citedJudging the Judges: A Systematic Study of Position Bias in LLM-as-a-Judge

6 citations · 6 across the 4 of their papers we have counts for

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cs.CL2026

When Debiasing Backfires: Counterintuitive Side Effects of Preprocessing-Based Stereotype Mitigation

Yahan Zheng, John Guerrerio, Soroush Vosoughi +1

Preprocessing-based methods for stereotype mitigation, such as pre-/post-training on debiased corpora, are widely used in NLP. While these approaches reduce measurable stereotypes…

cs.CL2026

Scalable and Culturally Specific Stereotype Dataset Construction via Human-LLM Collaboration

Weicheng Ma, John Guerrerio, Soroush Vosoughi

Research on stereotypes in large language models (LLMs) has largely focused on English-speaking contexts, due to the lack of datasets in other languages and the high cost of manual…

cs.CL20256 cited

Judging the Judges: A Systematic Study of Position Bias in LLM-as-a-Judge

Lin Shi, Chiyu Ma, Wenhua Liang +3

LLM-as-a-Judge has emerged as a promising alternative to human evaluators across various tasks, yet inherent biases - particularly position bias, the tendency to favor solutions ba…

cs.CL2025

A Generalizable Rhetorical Strategy Annotation Model Using LLM-based Debate Simulation and Labelling

Shiyu Ji, Farnoosh Hashemi, Joice Chen +9

Rhetorical strategies are central to persuasive communication, from political discourse and marketing to legal argumentation. However, analysis of rhetorical strategies has been li…

cs.CL2025

Communication is All You Need: Persuasion Dataset Construction via Multi-LLM Communication

Weicheng Ma, Hefan Zhang, Ivory Yang +8

Large Language Models (LLMs) have shown proficiency in generating persuasive dialogue, yet concerns about the fluency and sophistication of their outputs persist. This paper presen…

cs.CL2025

Is It Navajo? Accurate Language Detection in Endangered Athabaskan Languages

Ivory Yang, Weicheng Ma, Chunhui Zhang +1

Endangered languages, such as Navajo - the most widely spoken Native American language - are significantly underrepresented in contemporary language technologies, exacerbating the…