Papers by Dan Yuan
VisCGEC: Benchmarking the Visual Chinese Grammatical Error Correction (2025.naacl-long)
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| Challenge: | Existing studies on Chinese grammatical error correction ignore multi-modality and faked errors, which pushes techniques far away from real-world scenarios. |
| Approach: | They propose to benchmark Chinese grammatical error correction for Chinese as a foreign language learner (CFL) using a dataset, they propose to use two CGEC frameworks to conduct experiments . |
| Outcome: | The proposed approach achieves an F 0.5 score of only 28.9%. |
From Mimesis to Metamorphosis: Evolving VLM Judges via In-Context Comparing and Knowledge Internalization (2026.findings-acl)
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| Challenge: | Existing approaches to subjective assessment are inconsistent and inconsistent due to inconsistent scales and inherent preference biases. |
| Approach: | They propose a framework that operationalizes subjective assessment as comparative analysis and internalizes it via Language Buttons. |
| Outcome: | The proposed framework achieves state-of-the-art performance across multiple benchmarks and is scale-steerable. |
CtrlNews: LLM-based Multi-Agent Controllable News Writing via Knowledge Gravitational Field (2025.findings-emnlp)
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| Challenge: | Current approaches to news writing rely on superficially retrieved information and oversimplified knowledge enumeration resulting in shallow, repetitive, and unordered outputs. |
| Approach: | They propose an LLM-based multi-agent controllable news writing framework called CtrlNews . they propose a fine-grained viewpoint control mechanism to regulate bias, emotion, and exaggeration attributes. |
| Outcome: | The proposed framework simulates expert questioning through automated role assignment and question generation followed by a three-layer hierarchical gravitational graph iteratively refined via expansion-reflection cycles. |
MT-OSC: Path for LLMs that Get Lost in Multi-Turn Conversation (2026.findings-acl)
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Jyotika Singh, Fang Tu, Miguel Ballesteros, Weiyi Sun, Sandip Ghoshal, Michelle Yuan, Yassine Benajiba, Sujith Ravi, Dan Roth
| Challenge: | Large language models suffer performance degradation when user instructions and context are distributed over multiple conversational turns. |
| Approach: | They propose a framework that condenses chat history in the background without disrupting the user experience. |
| Outcome: | The proposed framework reduces token counts by up to 72% in 10-turn dialogues while remaining robust to distractors and irrelevant turns. |
Exploring Dual Encoder Architectures for Question Answering (2022.emnlp-main)
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| Challenge: | Dual encoders have been used for question-answering and information retrieval tasks with good results. |
| Approach: | They propose to use two different versions of dual encoders for QA retrieval tasks . they propose to share parameters in projection layers between two encoder towers . |
| Outcome: | The proposed architectures outperform SDE and ADE on QA retrieval tasks. |
Decoder-Only LLMs can be Masked Auto-Encoders (2025.acl-short)
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Dan Qiao, Yuan Gao, Zheming Yang, Di Yang, Ziheng Wu, Pengcheng Lu, Minghui Qiu, Juntao Li, Min Zhang
| Challenge: | Modern NLP workflows require different models for generation and embedding tasks. |
| Approach: | They propose a method that transforms an LLM into a Uni-Directional Masked Auto-Encoder. |
| Outcome: | The proposed method achieves state-of-the-art under unsupervised conditions with merely 100 training steps. |
Question Generation Based on Grammar Knowledge and Fine-grained Classification (2022.coling-1)
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| Challenge: | Recent research on question generation has achieved great success, but some question types and answers did not match. |
| Approach: | They construct a question type classifier and a query generator to solve the problem of question types not matching with other questions. |
| Outcome: | The proposed model improves the accuracy of interrogative words in generated questions. |