5 citations · 14 across the 17 of their papers we have counts for
13 papers · 1 filter
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…
Neural Dynamics of AI-attributed Irony Reveal a Partial Intentional Stance
Xiaohui Rao, Hanlin Wu, Zhenguang G. Cai
As Large Language Models (LLMs) are increasingly deployed as social agents and trained to produce humor and irony, a question emerges: when encountering witty AI remarks, do people…
A funny companion: Distinct neural responses to AI- versus human-attributed humor
Xiaohui Rao, Hanlin Wu, Zhenguang G. Cai
As artificial intelligence (AI) companions become capable of human-like communication, including telling jokes, understanding how people cognitively and affectively respond to AI-a…
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…
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…
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…
Large Language Models for Automated Literature Review: An Evaluation of Reference Generation, Abstract Writing, and Review Composition
Xuemei Tang, Xufeng Duan, Zhenguang G. Cai
Large language models (LLMs) have emerged as a potential solution to automate the complex processes involved in writing literature reviews, such as literature collection, organizat…
Speaker effects in language comprehension: An integrative model of language and speaker processing
Hanlin Wu, Zhenguang G. Cai
The identity of a speaker influences language comprehension through modulating perception and expectation. This review explores speaker effects and proposes an integrative model of…
HLB: Benchmarking LLMs' Humanlikeness in Language Use
Xufeng Duan, Bei Xiao, Xuemei Tang +1
As synthetic data becomes increasingly prevalent in training language models, particularly through generated dialogue, concerns have emerged that these models may deviate from auth…
Unveiling Language Competence Neurons: A Psycholinguistic Approach to Model Interpretability
Xufeng Duan, Xinyu Zhou, Bei Xiao +1
As large language models (LLMs) advance in their linguistic capacity, understanding how they capture aspects of language competence remains a significant challenge. This study ther…
Linguistic Minimal Pairs Elicit Linguistic Similarity in Large Language Models
Xinyu Zhou, Delong Chen, Samuel Cahyawijaya +2
We introduce a novel analysis that leverages linguistic minimal pairs to probe the internal linguistic representations of Large Language Models (LLMs). By measuring the similarity…
Grammaticality Representation in ChatGPT as Compared to Linguists and Laypeople
Zhuang Qiu, Xufeng Duan, Zhenguang G. Cai
Large language models (LLMs) have demonstrated exceptional performance across various linguistic tasks. However, it remains uncertain whether LLMs have developed human-like fine-gr…
MacBehaviour: An R package for behavioural experimentation on large language models
Xufeng Duan, Shixuan Li, Zhenguang G. Cai1
There has been increasing interest in investigating the behaviours of large language models (LLMs) and LLM-powered chatbots by treating an LLM as a participant in a psychological e…