collaborators

6 papers

cs.CL2026

Contextual Semantic Relevance Tracks fMRI BOLD Responses During Naturalistic Speech Comprehension

Kun Sun, Rong Wang

Naturalistic language comprehension requires listeners to process both local probabilistic expectations and contextual semantic relations. This study tested whether contextual sema…

cs.CL2026

Contextual Semantic Relevance and Word Surprisal Predict N400 and P600 Dynamics During Naturalistic Reading

Kun Sun, Rong Wang

Word surprisal is a well-established computational predictor of human neural responses during language comprehension, but it remains less clear whether local semantic fit explains…

cs.CL2026

Prior over Evidence: Stereotype-Driven Diagnosis in LLM-Based L2 Pronunciation Feedback

Rong Wang, Kun Sun

Large language models are increasingly deployed for written pronunciation feedback in second-language (L2) English learning, under the assumption that their diagnoses are grounded…

cs.CL2026

Stop When Further Reasoning Won't Help: Attention-State Adaptive Generation in Reasoning Models

Jiakai Li, Ke Qin, Rongzheng Wang +4

By incorporating test-time compute scaling, large reasoning models (LRMs) can solve complex problems through explicit chain-of-thought (CoT) reasoning processes. However, they ofte…

cs.CL2025

Systematic Framework of Application Methods for Large Language Models in Language Sciences

Kun Sun, Rong Wang

Large Language Models (LLMs) are transforming language sciences. However, their widespread deployment currently suffers from methodological fragmentation and a lack of systematic s…

cs.CL2025

The fragility of "cultural tendencies" in LLMs

Kun Sun, Rong Wang

In a recent study, Lu, Song, and Zhang (2025) (LSZ) propose that large language models (LLMs), when prompted in different languages, display culturally specific tendencies. They re…