Shuqi Wang ☰

About Me

Shuqi Wang

Shuqi (Eliza) Wang 王書琪

I completed my PhD in September 2026 and am on the job market for postdoctoral and research assistant professor positions (2027 start) in computational psycholinguistics, human–LLM comparison and LLM evaluation.

I received my PhD in Linguistics from The Chinese University of Hong Kong in 2026 (thesis Predictive Language Processing in Humans and in Large Language Models, passed in September 2026; degree to be conferred in March 2027), working in the Language Processing Lab under Prof. Zhenguang G. Cai. My doctoral work was supported by the Hong Kong PhD Fellowship and the CUHK Vice-Chancellor’s HKPFS Scholarship.

My research asks how humans and large language models predict upcoming language, and what controlled comparisons between them can and cannot explain. I pair eye-tracking (the visual world paradigm), spoken-language and sentence-completion experiments in Mandarin with matched evaluations of text, vision–language and speech–language models (GPT-4o, Llama 3.2, Qwen2.5, LLaVA-1.5, Qwen2.5-Omni), including layer-wise and cross-modal attention analyses. The recurring finding is that models predict what humans predict, but by different routes: they weight sentence structure several times more strongly, deploy some cues less reliably, and show later cue effects. My thesis develops a framework for reading such comparisons — the LLM as measurement tool, candidate cognitive model or contrastive model — together with a checklist for reporting what a comparison licenses.

Current directions: human-grounded benchmarks for Chinese LLMs and speech–language models (including a Mandarin phonological minimal-pair benchmark); interpretability beyond attention (attention versus gradient-based attribution, and the faithfulness of model explanations); and what speech adds to language-model–brain alignment, using text-only and audio-conditioned readouts from a single speech–text LLM as encoding models of naturalistic Mandarin MEG. I am also interested in prediction under cognitive load during consecutive interpreting.

Before my PhD, I studied second-language processing and bilingualism at Peking University (M.A.) and Nanjing University (B.A.), both in teaching Chinese as a second language. I also co-founded the Computational Neurolinguistics Forum (计算神经语言学; about 7,900 followers, 14 online talks in 2025–26), an academic platform bridging psycholinguistics, neuroscience and computational modelling for the Chinese-speaking research community.

Email: shuqiwang@link.cuhk.edu.hk
Google Scholar · ORCID · GitHub

Curated Research

VWP time-course
Similar Predictions, Different Processes: Comparing Human and Multimodal LLM Prediction

Using the visual world paradigm with spoken Mandarin and visual displays, we show that humans and Qwen2.5-Omni produce similar prediction outputs but differ in processing dynamics — a structure effect about three times the human effect, a later structural sensitivity and anomalous cue integration in the model reveal algorithmic-level divergence beneath computational-level convergence.

CoNLL 2026 @ ACL →   Code →
DO/PO structure
What to Predict? Sentence Structure Influences Contrast Predictions in Humans and LLMs

Mandarin dative structures (DO/PO) modulate contrastive focus predictions differently in humans and LLMs. Both show the same direction of structural effects, but models amplify them 3× relative to humans — and correlate more with spoken than written human data. 🏆 Best Paper Nomination.

CMCL 2025 @ NAACL →
LLaVA attention
A Multimodal LLM "Foresees" Objects Based on Verb Information but Not Gender

LLaVA-1.5 shows verb-based anticipatory attention in visual scenes — paralleling human VWP patterns — but fails at gender-based prediction. Layer-wise analysis reveals middle transformer layers are most responsible for verb-driven predictive behavior.

CoNLL 2024 @ EMNLP →

Publications

2025
Wang, S., Duan, X., & Cai, Z. G.
CMCL 2025 @ NAACL 🏆 Best Paper Nomination
2024
Wang, S., Duan, X., & Cai, Z. G.
CoNLL 2024 @ EMNLP, 435–441
2024
Cai, Z. G., Duan, X., Haslett, D., Wang, S., & Pickering, M. J.
CMCL 2024 @ ACL, 37–56 · 200+ citations
2020
Jiang, S., Lu, S., & Wang, S.
Journal of Chinese Language Education, 18(2), 1–21
2018
Wu, M., Wang, S., & He, W.
Chinese Language Globalization Studies, (01), 163–173
prep
What large language models can and cannot explain about human predictive language processing: A role-sensitive, Marr-informed framework
Wang, S. & Cai, Z. G.
Theoretical article developed from the thesis In Preparation

Invited Talks

Wang, S. (2026, October). Do LLMs predict language like humans? What controlled comparisons can and cannot explain. AI Ethics and Governance Lab, Center for AI Research (CAiRE), The Hong Kong University of Science and Technology (host: Prof. Janet H. Hsiao).

Conference Talks

Wang, S. & Cai, Z. G. (2026). Similar predictions, different processes: A multi-level comparison of human and multimodal LLM language prediction. CoNLL 2026 @ ACL. San Diego, CA (online).
Wang, S., Duan, X., & Cai, Z. G. (2025). What to predict? Exploring how sentence structure influences contrast predictions in humans and large language models. CMCL 2025 @ NAACL. Albuquerque, NM. 🏆 Best Paper Nom.
Wang, S., Duan, X., & Cai, Z. G. (2024). A multimodal large language model "foresees" objects based on verb information but not gender. CoNLL 2024 @ EMNLP. Miami, FL.
Wu, M., Wang, S., & He, W. (2018). The effects of language background on listener's perception, intelligibility and comprehensibility of accented Chinese speech. 14th International Conference on Chinese Language Teaching.

Poster Presentations

Wang, S. & Cai, Z. G. (2026). Structure–prosody integration in predictive processing: A human–multimodal LLM comparison in the visual world paradigm. HSP 2026. Massachusetts Institute of Technology.
Wang, S., Chen, X., & Cai, Z. G. (2024). Fewer predictions in L1 source language in consecutive interpreting when cognitive load is high. HSP 2024. University of Michigan.
Wang, S., Chen, X., & Cai, Z. G. (2023). Fewer predictions in L1 source language in consecutive interpreting when cognitive load is high. AMLaP Asia 2023. The Chinese University of Hong Kong.

Teaching & Service

Teaching assistant, CUHK: LING5607 Topics in Psycholinguistics (postgraduate; 2024–25 and 2025–26; tutorials on experimental design and statistics in R); LING3208 Language Disorders (2021–22); LING1003 Exploring Grammar: English (2021–22). Annual hands-on eye-tracking workshop (EyeLink 1000 Plus) for about 50 postgraduate students since 2022.
Earlier teaching: Lecturer of Chinese, Columbia in Beijing Program (2019; bonus for outstanding teaching); Language Partner, Yenching Academy, Peking University (2018–19).
Organisation and outreach: co-founder of the Computational Neurolinguistics Forum (2022–); local organising team, AMLaP Asia 2023 (CUHK); co-organiser, CogSci 2023 Hong Kong Meetup & Workshop: Cognition, Language and AI.
Selected awards: Hong Kong PhD Fellowship (2021–24); CUHK Faculty of Arts PhD Support Scheme (2024–25); Best Paper nomination, CMCL 2025; finalist, CUHK Three Minute Thesis Competition 2026.