activity
20242026
most citedHLB: Benchmarking LLMs' Humanlikeness in Language Use

1 citations · 1 across the 5 of their papers we have counts for

collaborators

10 papers

cs.CL2026

Do Large Language Models Plan Answer Positions? Position Bias in Multiple-Choice Question Generation

Xuemei Tang, Xufeng Duan, Zhenguang G. Cai

Large language models (LLMs) are increasingly used to generate multiple-choice questions (MCQs), where correct answers should ideally be uniformly distributed across options. Howev…

cs.LG2026

SCALPEL: Selective Capability Ablation via Low-rank Parameter Editing for Large Language Model Interpretability Analysis

Zihao Fu, Xufeng Duan, Zhenguang G. Cai

Large language models excel across diverse domains, yet their deployment in healthcare, legal systems, and autonomous decision-making remains limited by incomplete understanding of…

cs.CL2026

Locate, Steer, and Improve: A Practical Survey of Actionable Mechanistic Interpretability in Large Language Models

Hengyuan Zhang, Zhihao Zhang, Mingyang Wang +26

Mechanistic Interpretability (MI) has emerged as a vital approach to demystify the opaque decision-making of Large Language Models (LLMs). However, existing reviews primarily treat…

cs.CL2025

Information Loss in LLMs' Multilingual Translation: The Role of Training Data, Language Proximity, and Language Family

Yumeng Lin, Xufeng Duan, David Haslett +2

Large language models have achieved impressive progress in multilingual translation, yet they continue to face challenges with certain language pairs-particularly those with limite…

cs.CL2025

How Syntax Specialization Emerges in Language Models

Xufeng Duan, Zhaoqian Yao, Yunhao Zhang +2

Large language models (LLMs) have been found to develop surprising internal specializations: Individual neurons, attention heads, and circuits become selectively sensitive to synta…

cs.CL2025

Distinct social-linguistic processing between humans and large audio-language models: Evidence from model-brain alignment

Hanlin Wu, Xufeng Duan, Zhenguang Cai

Voice-based AI development faces unique challenges in processing both linguistic and paralinguistic information. This study compares how large audio-language models (LALMs) and hum…