Papers by Mingyu Huang
MAssistant: A Personal Knowledge Assistant for MOOC Learners (D19-3)
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| Challenge: | Massive Open Online Courses (MOOCs) have experienced a rapid development since 2012 . many MOOC platforms have been launched, including Coursera1 , edX2 , and Udacity3 etc. |
| Approach: | They present a personal knowledge assistant system called MAssistant for MOOC learners . MAsistants has a large-scale concept graph built from open data . it also provides a browser extension which interacts with users during video lectures . |
| Outcome: | The proposed system helps users trace the concepts they have learned in MOOCs, and to build their own concept graphs. |
Exploring Concept Depth: How Large Language Models Acquire Knowledge and Concept at Different Layers? (2025.coling-main)
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Mingyu Jin, Qinkai Yu, Jingyuan Huang, Qingcheng Zeng, Zhenting Wang, Wenyue Hua, Haiyan Zhao, Kai Mei, Yanda Meng, Kaize Ding, Fan Yang, Mengnan Du, Yongfeng Zhang
| Challenge: | Large language models have shown remarkable performances across a wide range of tasks, but mechanisms by which they encode tasks of varying complexity remain poorly understood. |
| Approach: | They propose to explore the possibility that LLMs process concepts in different layers . they propose to categorize concepts based on their level of abstraction . |
| Outcome: | The proposed model can process complex concepts in shallow layers, the authors show . the proposed model could be used to prob complex tasks in shallow ones . |
From Prediction to Intervention: Personalized Meal-Level Glucose Regulation via an LLM Agent (2026.findings-acl)
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| Challenge: | Existing approaches to individualized glucose regulation are generic and do not account for individual-specific glucose dynamics. |
| Approach: | They propose a physio-feedback agentic loop that integrates individualized absorption modeling with dietary intervention to regulate glucose response. |
| Outcome: | The proposed system improves prediction accuracy and reduces glucose excursions. |
Affection Driven Neural Networks for Sentiment Analysis (2020.lrec-1)
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| Challenge: | Existing deep neural network models lack mechanisms to highlight important sentiment terms. |
| Approach: | They propose a method to incorporate affective knowledge into deep neural network models by mapping affective influence vectors to an affective impact value and integrating them into long-term memory models to highlight affective terms. |
| Outcome: | The proposed approach improves on three large datasets by 1.0% to 1.5% on the benchmark datasets. |
Sina Mandarin Alphabetical Words:A Web-driven Code-mixing Lexical Resource (2020.aacl-main)
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| Challenge: | Mandarin Alphabetical Words (MAWs) are a key component of Modern Chinese . they are characterized by unique code-mixing idiosyncrasies influenced by language exchanges . |
| Approach: | They propose to construct a large collection of Mandarin Alphabetic Words from Sina Weibo . they propose to use a web-based technique to identify and validate MAWs . |
| Outcome: | The proposed method identifies 16,207 Mandarin Alphabetic Words (MAWs) using a web-based technique . the results show that the proposed method is useful for linguistic research and inquiries . |