Mengyang Qiu

Mengyang Qiu

he/him

Language, cognition, and computation.

Publications

You can also find my articles on my Google Scholar profile.

Language and Cognition

Paired-Comparison Versus Cross-Sectional Approaches for Assessing Longitudinal Speech Change in Parkinson’s Disease Following Deep Brain Stimulation of the Subthalamic Nucleus

Published in American Journal of Speech-Language Pathology, 2026

This study evaluated a paired-comparison method for detecting longitudinal speech change in Parkinson’s disease (PD) across three perceptual constructs (intelligibility, listener effort, and severity) relative to a cross-sectional method for detecting speech changes in the same cohort.

Recommended citation: Manes, J. L., Rohl, A. H., Qiu, M., Stipancic, K. L., Corcos, D. M., Patterson, C. G., Greenlee, J. D. W., & Tjaden, K. (2026). Paired-comparison versus cross-sectional approaches for assessing longitudinal speech change in Parkinson's disease following deep brain stimulation of the subthalamic nucleus. American Journal of Speech-Language Pathology, 35(5), 2243-2261. https://doi.org/10.1044/2026_AJSLP-26-00092

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Can LLMs Simulate Human Behavioral Variability? A Case Study in the Phonemic Fluency Task

Published in Proceedings of the 15th Workshop on Cognitive Modeling and Computational Linguistics, 2026

This study examines whether LLMs can approximate individual differences in the phonemic fluency task, where participants generate words beginning with a target letter.

Recommended citation: Qiu, M., Brisebois, Z., & Sun, S. (2026). Can LLMs simulate human behavioral variability? A case study in the phonemic fluency task. Proceedings of the 15th Workshop on Cognitive Modeling and Computational Linguistics, 250–263. https://doi.org/10.63317/3ncnwh7mvnfp

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Progress Toward Estimating the Minimal Clinically Important Difference of Intelligibility: A Crowdsourced Perceptual Experiment

Published in Journal of Speech, Language, and Hearing Research, 2025

The purpose of the current study was to estimate the minimal clinically important difference (MCID) of sentence intelligibility in control speakers and in speakers with dysarthria due to multiple sclerosis (MS) and Parkinson's disease (PD).

Recommended citation: Stipancic, K. L., van Brenk, F., Qiu, M., & Tjaden, K. (2025). Progress toward estimating the minimal clinically important difference of intelligibility: A crowdsourced perceptual experiment. Journal of Speech, Language, and Hearing Research, 68(7S), 3480–3494. https://doi.org/10.1044/2024_JSLHR-24-00354

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A Network Analysis of the Semantic Evolution of ‘Fruit’ and ‘Stone’ in Tibeto-Burman Languages

Published in Poznan Studies in Contemporary Linguistics, 2025

The lexemes ‘fruit’ and ‘stone’ are known as the origins of the numeral classifiers for small round objects in many Tibeto-Burman languages. This paper employs a correlation-based network construction method to investigate the colexification networks of the two concepts in 58 + 68 Tibeto-Burman languages.

Recommended citation: Li, Y., & Qiu, M. (2025). A network analysis of the semantic evolution of ‘fruit’ and ‘stone’ in Tibeto-Burman languages. Poznan Studies in Contemporary Linguistics, 61(2), 121–172. https://doi.org/10.1515/psicl-2024-0024

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Estimating Type of Print Exposure Across Aging Through Author Production

Published in Proceedings of the Annual Meeting of the Cognitive Science Society, 2024

This study introduces a novel approach for quantifying individual differences in print exposure through the integration of distributional semantics with the Author Production Test (APT).

Recommended citation: Qiu, M., Castro, N., & Johns, B. T. (2024). Estimating type of print exposure across aging through author production. Proceedings of the Annual Meeting of the Cognitive Science Society, 46, 1175–1181. https://escholarship.org/uc/item/41z38291

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Structural Comparisons of Noun and Verb Networks in the Mental Lexicon

Published in Proceedings of the Annual Meeting of the Cognitive Science Society, 2021

The current study uses network science tools to quantitatively investigate the structural differences of noun and verb categories.

Recommended citation: Qiu, M., Castro, N., & Johns, B. T. (2021). Structural comparisons of noun and verb networks in the mental lexicon. Proceedings of the Annual Meeting of the Cognitive Science Society, 43, 1649-1655. https://escholarship.org/uc/item/4b20s6wp

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Semantic Diversity in Paired-Associate Learning: Further Evidence for the Information Accumulation Perspective of Cognitive Aging

Published in Psychonomic Bulletin & Review, 2020

The current study used a within-subject design in order to examine the influence of linguistic experience on paired associate learning in younger and older adults. Linguistic experience was modeled using a semantic diversity measure of word strength. When frequency is controlled for, high semantic diversity words are associated to a greater number of words and have a higher average strength of association. In the current study, PAL performance of older adults was significantly lower for word pairs involving high semantic diversity words, while their performance did not differ for low semantic diversity words, consistent with the information accumulation perspective of aging.

Recommended citation: Qiu, M., & Johns, B. T. (2020). Semantic diversity in paired-associate learning: Further evidence for the information accumulation perspective of cognitive aging. Psychonomic Bulletin & Review, 27(1), 114–121. https://doi.org/10.3758/s13423-019-01691-w

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Natural Language Processing

Bias in Large Language Models: Origin, Evaluation, and Mitigation

Published in Electronics, 2026

Large language models (LLMs) have revolutionized natural language processing, but their susceptibility to biases poses significant challenges. This comprehensive review examines the landscape of bias in LLMs, from its origins to current mitigation strategies. By synthesizing current knowledge on bias in LLMs, this review contributes to the ongoing effort to develop fair and responsible artificial intelligence (AI) systems.

Recommended citation: Guo, Y., Guo, M., Su, J., Yang, Z., Zhu, M., Li, H., Qiu, M., & Liu, S. S. (2026). Bias in large language models: Origin, evaluation, and mitigation. Electronics, 15(9), 1824. https://doi.org/10.3390/electronics15091824

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Multilingual Grammatical Error Annotation: Combining Language-Agnostic Framework With Language-Specific Flexibility

Published in Proceedings of the 20th Workshop on Innovative Use of NLP for Building Educational Applications, 2025

In this paper, we introduce a standardized, modular framework for multilingual grammatical error annotation.

Recommended citation: Qiu, M., Nguyen, T. M., Huang, Z., Li, Z., Gu, Y., Gao, Q., Liu, S., & Park, J. (2025). Multilingual grammatical error annotation: Combining language-agnostic framework with language-specific flexibility. Proceedings of the 20th Workshop on Innovative Use of NLP for Building Educational Applications, 202–212. https://doi.org/10.18653/v1/2025.bea-1.15

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Refined Evaluation for End-to-End Grammatical Error Correction Using an Alignment-Based Approach

Published in Proceedings of the 31st International Conference on Computational Linguistics, 2025

We propose a refined alignment-based method to assess end-to-end grammatical error correction (GEC) systems, aiming to reproduce and improve results from existing evaluation tools, such as ERRANT, even when applied to raw text input—reflecting real-world language learners’ writing scenarios.

Recommended citation: Wang, J., Qiu, M., Gu, Y., Huang, Z., & Park, J. (2025). Refined evaluation for end-to-end grammatical error correction using an alignment-based approach. Proceedings of the 31st International Conference on Computational Linguistics, 774–785. https://aclanthology.org/2025.coling-main.52/

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Improving Automatic Grammatical Error Annotation for Chinese Through Linguistically-Informed Error Typology

Published in Proceedings of the 31st International Conference on Computational Linguistics, 2025

This paper introduces improvements to automatic grammatical error annotation for Chinese. Our refined framework addresses language-specific challenges that cause common spelling errors in Chinese, including pronunciation similarity, visual shape similarity, specialized participles, and word ordering.

Recommended citation: Gu, Y., Huang, Z., Zeng, M., Qiu, M., & Park, J. (2025). Improving automatic grammatical error annotation for Chinese through linguistically-informed error typology. Proceedings of the 31st International Conference on Computational Linguistics, 2781–2798. https://aclanthology.org/2025.coling-main.189/

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Evaluating Prompting Strategies for Grammatical Error Correction Based on Language Proficiency

Published in Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation, 2024

This paper proposes an analysis of prompting strategies for grammatical error correction (GEC) with selected large language models (LLM) based on language proficiency.

Recommended citation: Zeng, M., Kuang, J., Qiu, M., Song, J., & Park, J. (2024). Evaluating prompting strategies for grammatical error correction based on language proficiency. Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation, 6426–6430. https://aclanthology.org/2024.lrec-main.569

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Artificial Error Generation with Fluency Filtering

Published in Proceedings of the 14th Workshop on Innovative Use of NLP for Building Educational Applications, 2019

The present study explores how fluency filtering can affect the quality of artificial errors.

Recommended citation: Qiu, M., & Park, J. (2019). Artificial error generation with fluency filtering. Proceedings of the 14th Workshop on Innovative Use of NLP for Building Educational Applications, 87–91. https://doi.org/10.18653/v1/W19-4408

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Improving Precision of Grammatical Error Correction with a Cheat Sheet

Published in Proceedings of the 14th Workshop on Innovative Use of NLP for Building Educational Applications, 2019

In this paper, we explore two approaches of generating error-focused phrases and examine whether these phrases can lead to better performance in grammatical error correction.

Recommended citation: Qiu, M., Chen, X., Liu, M., Parvathala, K., Patil, A., & Park, J. (2019). Improving precision of grammatical error correction with a cheat sheet. Proceedings of the 14th Workshop on Innovative Use of NLP for Building Educational Applications, 240-245. https://doi.org/10.18653/v1/W19-4425

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