2 citations · 3 across the 13 of their papers we have counts for
14 papers
What Does an LLM Learn from Reinforcement Learning? A Mechanistic Interpretability Perspective with Fixed-SAE Track
Lingheng Du, Yiming Tang, Xufeng Duan +1
Reinforcement learning (RL) is widely utilized in large language model training to improve targeted capabilities, yet how RL reshapes a model remains poorly understood. Prior attem…
Capsule Lens: Locating and Tracking Concept Geometry in Model Representations
Yiming Tang, Harshvardhan Saini, Samyak Jha +3
Understanding how concepts are encoded in the internal representations of machine learning models is a central problem in mechanistic interpretability, essential both for the scien…
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…
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…
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…
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…
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…