Papers by Mingyang Wan
Curriculum Knowledge Distillation for Emoji-supervised Cross-lingual Sentiment Analysis (2022.emnlp-main)
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| Challenge: | Existing sentiment analysis models do not have sufficient sentiment corpus to detect sentiment in low-resource languages. |
| Approach: | They propose a cross-lingual sentiment analysis approach to transfer sentiment knowledge across languages . they use emojis to bridge the source and target languages to find the sentiment . |
| Outcome: | The proposed approach bridges the source and target languages using emojis . it can learn delicate sentiment knowledge, avoiding cross-lingual gaps . |
PFDial: A Structured Dialogue Instruction Fine-tuning Method Based on UML Flowcharts (2025.findings-acl)
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Ming Zhang, Yuhui Wang, Yujiong Shen, Tingyi Yang, Changhao Jiang, Yilong Wu, Shihan Dou, Qinhao Chen, Zhiheng Xi, Zhihao Zhang, Yi Dong, Zhen Wang, Zhihui Fei, Mingyang Wan, Tao Liang, Guojun Ma, Qi Zhang, Tao Gui, Xuanjing Huang
| Challenge: | Large Language Models (LLMs) have shown remarkable progress in dialogue and reasoning, but they struggle to solve strictly constrained dialogue tasks. |
| Approach: | They construct a dataset that contains 12,705 high-quality Chinese dialogue instructions from 440 flowcharts containing 5,055 process nodes. |
| Outcome: | The proposed model outperforms GPT-4o models on backward transitions and outperformed GPT-42 models on the same dataset. |
COMPASS: Enhancing Agent Long-Horizon Reasoning with Evolving Context (2026.acl-long)
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| Challenge: | Long-horizon tasks that require sustained reasoning and multiple tool interactions remain challenging for LLM agents. |
| Approach: | They propose a framework that separates tactical execution, strategic oversight, and context organization into three specialized components. |
| Outcome: | The proposed framework improves accuracy by 20% relative to baselines on GAIA, BrowseComp, and Humanity’s Last Exam tasks. |
Route Sparse Autoencoder to Interpret Large Language Models (2025.emnlp-main)
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| Challenge: | Sparse autoencoders (SAEs) extract interpretable and monosemantic features in large language models . prior work focused on feature extraction from a single layer, failing to capture activations that span multiple layers. |
| Approach: | They propose a framework that integrates a routing mechanism with a shared SAE to efficiently extract features from multiple layers. |
| Outcome: | The proposed framework extracts features from multiple layers while incurring minimal parameter overhead while achieving high interpretability and flexibility. |
AdapThink: Adaptive Thinking Preferences for Reasoning Language Models (2026.findings-acl)
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| Challenge: | Recent research has highlighted a significant inefficiency associated with the slow thinking paradigm . models often overthink simple tasks while underthinking complex challenges . |
| Approach: | They propose a framework for adaptive reasoning preference control that dynamically adjusts reflection preferences based on group-level distributional statistics of reasoning length and reflection intensity. |
| Outcome: | The proposed framework reduces average response length by 17.1%-21.4% while improving performance by 6.12-6.59 points under 32K token budgets. |