Papers by Mingyu Chen
An LLM-Embedding Semantic Adaptation Network for Post-level Semantic Drift Evaluation (2026.findings-acl)
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| Challenge: | Evaluating semantic drift is essential for understanding discourse evolution and opinion formation in online discussions. |
| Approach: | They propose an LLM-embedding Semantic Adaptation Network to evaluate semantic drift . they use an LRU module, an LEM-Embedding graph convolutional network module and an adaptive fusion module to integrate features from event related posts. |
| Outcome: | The proposed model achieves state-of-the-art performance on the semantic drift evaluation task compared to baseline models. |
Instructions as Backdoors: Backdoor Vulnerabilities of Instruction Tuning for Large Language Models (2024.naacl-long)
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| Challenge: | et al., 2021) show that instruction models can be trained on crowdsourced datasets with task instructions to achieve superior performance. |
| Approach: | They examine security concerns of emergent instruction tuning paradigm that models are trained on crowdsourced datasets with task instructions to achieve superior performance. |
| Outcome: | The proposed model can achieve 90% success rate across four commonly used datasets. |
LongLeader: A Comprehensive Leaderboard for Large Language Models in Long-context Scenarios (2025.naacl-long)
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Pei Chen, Hongye Jin, Cheng-Che Lee, Rulin Shao, Jingfeng Yang, Mingyu Zhao, Zhaoyu Zhang, Qin Lu, Kaiwen Men, Ning Xie, Huasheng Li, Bing Yin, Han Li, Lingyun Wang
| Challenge: | LongLeader aims to assess different LLMs' long-context comprehension abilities . long-constext comprehension is a key bottleneck for many use cases . |
| Approach: | They propose a leaderboard to assess different LLMs' long-context comprehension abilities . they offer open-source access to the benchmarks and maintain a dedicated website . |
| Outcome: | The proposed model assesses different LLMs on selected benchmarks and provides open-source access to the benchmarks. |
LongReD: Mitigating Short-Text Degradation of Long-Context Large Language Models via Restoration Distillation (2025.acl-long)
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| Challenge: | Large language models (LLMs) have extended context windows through scaling positional encodings and lightweight continual pre-training, but performance degradation is still not fully explored. |
| Approach: | They propose a novel approach to reduce short-text performance degradation by minimizing distribution drift in hidden states and attention scores. |
| Outcome: | The proposed approach minimizes the distribution discrepancy between the extended and original models while maintaining or even enhancing the model's long-context abilities. |
GigaSpeech 2: An Evolving, Large-Scale and Multi-domain ASR Corpus for Low-Resource Languages with Automated Crawling, Transcription and Refinement (2025.acl-long)
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Yifan Yang, Zheshu Song, Jianheng Zhuo, Mingyu Cui, Jinpeng Li, Bo Yang, Yexing Du, Ziyang Ma, Xunying Liu, Ziyuan Wang, Ke Li, Shuai Fan, Kai Yu, Wei-Qiang Zhang, Guoguo Chen, Xie Chen
| Challenge: | GigaSpeech 2 is a large-scale, multi-domain, multilingual speech recognition corpus for low-resource languages. |
| Approach: | They propose a large-scale, multi-domain, multilingual speech recognition corpus for low-resource languages and an automated pipeline for data crawling, transcription, and label refinement. |
| Outcome: | The proposed corpus reduces the word error rate for Thai, Indonesian, and Vietnamese on a realistic YouTube test set by 25% to 40% compared to Whisper large-v3. |
CATCH: A Novel Data Synthesis Framework for High Therapy Fidelity and Memory-Driven Planning Chain of Thought in AI Counseling (2025.findings-emnlp)
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| Challenge: | Existing studies employ a one-time generation approach to synthesize multi-turn dialogue samples, resulting in low therapy fidelity and failing to capture decision-making rationale behind each response. |
| Approach: | They propose a data synthesis framework that synthesizes multi-turn dialogue samples and incrementally generates stage-aligned counseling dialogues. |
| Outcome: | The proposed framework significantly improves therapy fidelity and logical coherence in AI counseling. |
ShortGPT: Layers in Large Language Models are More Redundant Than You Expect (2025.findings-acl)
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Xin Men, Mingyu Xu, Qingyu Zhang, Qianhao Yuan, Bingning Wang, Hongyu Lin, Yaojie Lu, Xianpei Han, Weipeng Chen
| Challenge: | Recent studies have identified significant redundancy in large language models . quantization and pruning are two methods that reduce computational resources . |
| Approach: | They propose simple pruning methods that prune redundant layers based on their BI scores. |
| Outcome: | The proposed pruning methods demonstrate superior performance over previous pruning methods. |
Decoding Susceptibility: Modeling Misbelief to Misinformation Through a Computational Approach (2024.emnlp-main)
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| Challenge: | Existing studies on susceptibility to misinformation rely on self-reported beliefs, which can be subject to bias, expensive to collect, and challenging to scale for downstream applications. |
| Approach: | They propose a computational approach to efficiently model users’ latent susceptibility levels by using demographic factors and political ideology as inputs. |
| Outcome: | The proposed model shows that political leanings and other psychological factors exhibit varying degrees of association with susceptibility to COVID-19 misinformation. |
Speech Discrete Tokens or Continuous Features? A Comparative Analysis for Spoken Language Understanding in SpeechLLMs (2025.emnlp-main)
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| Challenge: | Speech Large Language Models (SpeechLLMs) have emerged as dominant speech processing approaches. |
| Approach: | They compare self-supervised learning-based discrete and continuous features . they compare performance across six spoken language understanding-related tasks . |
| Outcome: | The proposed models outperform discrete tokens and continuous features in six spoken language understanding-related tasks. |
Instructional Fingerprinting of Large Language Models (2024.naacl-long)
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| Challenge: | Large language models (LLMs) require considerable cost to train from scratch . fingerprinting is essential to protect intellectual property and to ensure downstream users and developers adhere to their license terms. |
| Approach: | They propose a method for fingerprinting Large language models that implants a private key into the model to generate specific text when the key is present. |
| Outcome: | The proposed method prevents publisher overclaim and maintains robustness against fingerprint guessing and parameter-efficient training. |
Weights-Rotated Preference Optimization for Large Language Models (2025.emnlp-main)
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Chenxu Yang, Ruipeng Jia, Mingyu Zheng, Naibin Gu, Zheng Lin, Siyuan Chen, Weichong Yin, Hua Wu, Weiping Wang
| Challenge: | Existing methods to align large language models with high reward hacking are limited by the complexity of the parameter space and the complexity. |
| Approach: | They propose a weights-rotated preference optimization algorithm that constrains the output layer logits with the KL divergence inherited from DPO and fine-tunes the intermediate hidden states. |
| Outcome: | The proposed algorithm achieves a 3.27-point improvement on AlpacaEval 2 and surpasses the best baseline by 6.2 to 7.5 points on MT-Bench with merely 0.015% of the trainable parameters. |
RevPRAG: Revealing Poisoning Attacks in Retrieval-Augmented Generation through LLM Activation Analysis (2025.findings-emnlp)
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| Challenge: | Retrieval-Augmented Generation (RAG) enriches the input to LLMs by retrieving information from the relevant knowledge database. |
| Approach: | They propose to use a knowledge database to enrich the input of LLMs by retrieving information from the relevant knowledge database. |
| Outcome: | The proposed approach can achieve 98% true positive rate while maintaining a false positive rate close to 1%. |