Papers by Mingyuan Zhang
Friendly Topic Assistant for Transformer Based Abstractive Summarization (2020.emnlp-main)
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| Challenge: | Abstractive document summarization is a comprehensive task in natural language processing. |
| Approach: | They propose a topic assistant that rearranges and learns document semantics . they propose TA that is compatible with Transformer-based models and user-friendly . |
| Outcome: | The proposed model is compatible with Transformer-based models and user-friendly. |
Cautious Next Token Prediction (2025.findings-acl)
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Yizhou Wang, Lingzhi Zhang, Yue Bai, Mang Tik Chiu, Zhengmian Hu, Mingyuan Zhang, Qihua Dong, Yu Yin, Sohrab Amirghodsi, Yun Fu
| Challenge: | Existing methods for decoding autoregressive models are temperature scaling and nucleus sampling to balance diversity and coherence. |
| Approach: | They propose a training-free decoding strategy that uses a model with a low perplexity score to select the trial with the lowest perplexities as the most probable and reliable path. |
| Outcome: | The proposed approach outperforms existing standard decoding strategies consistently by a clear margin. |
ALLSH: Active Learning Guided by Local Sensitivity and Hardness (2022.findings-naacl)
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| Challenge: | Existing studies show that labeling in crowdsourcing annotations is not an annotation artifact but rather a core linguistic phenomenon. |
| Approach: | They propose to retrieve unlabeled data with a local sensitivity and hardness-aware acquisition function. |
| Outcome: | The proposed method achieves consistent gains over the commonly used active learning strategies in various classification tasks. |
Distorted or Fabricated? A Survey on Hallucination in Video LLMs (2026.findings-acl)
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| Challenge: | Despite significant advances in video-language modeling, hallucinations remain a persistent challenge in video large language models. |
| Approach: | They present a systematic taxonomy that categorizes hallucinations into two core types: dynamic distortion and content fabrication. |
| Outcome: | The proposed taxonomy categorizes hallucinations into two core types: dynamic distortion and content fabrication. |
Foreseeing the Benefits of Incidental Supervision (2021.emnlp-main)
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| Challenge: | Real-world applications often require improved models by leveraging a range of cheap incidental supervision signals. |
| Approach: | They propose a unified PAC-Bayesian motivated informativeness measure that characterizes the uncertainty reduction provided by incidental supervision signals. |
| Outcome: | The proposed measure quantifies the value added by incidental supervision signals to sequence tagging tasks. |
UNIVID: Unified Vision-Language Model for Video Moderation (2026.acl-industry)
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Kejuan Yang, Yizhuo Zhang, Mingyuan Du, Yue Zhang, Dixin Zheng, Kaili Zhao, Yang Xiao, Hanzhong Liang, Kenan Xiao
| Challenge: | Existing video moderation systems rely on fragmented black-box classification models that are difficult to maintain and lack transparency. |
| Approach: | They propose a Unified Vision-Language model for Video Moderation that generates policy-aware captions that serve as an interpretable intermediate representation. |
| Outcome: | The proposed model reduces violation leakage and overkill rate by 42.7% while reducing maintenance costs. |
Cache-of-Thought: Master-Apprentice Framework for Cost-Effective Vision Language Model Reasoning (2025.emnlp-main)
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Mingyuan Wu, Jize Jiang, Haozhen Zheng, Meitang Li, Zhaoheng Li, Beitong Tian, Bo Chen, Yongjoo Park, Minjia Zhang, ChengXiang Zhai, Klara Nahrstedt
| Challenge: | Recent Vision Language Models (VLMs) have shown tremendous promise in a wide range of realworld applications, but their size has made at-scale deployment and operation challenging due to high consumption of cloud computing resource, high latency, and expensive API calls. |
| Approach: | They propose a master–apprentice framework for collaborative inference between large and small vision language models. |
| Outcome: | The proposed framework improves reasoning performance on widely-recognized and challenging general reasoning benchmarks and specifically boosts reasoning of apprentice VLMs by 36.6%. |
EnsLM: Ensemble Language Model for Data Diversity by Semantic Clustering (2021.acl-long)
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| Challenge: | Existing studies have shown that data diversity affects the performance of LMs if we train a single LM over the entire dataset. |
| Approach: | They propose an autoencoding topic model with a mixture prior to perform clustering for the data. |
| Outcome: | The proposed model can learn knowledge from different samples while extracting cluster-specific features. |