Papers by Lingwei Chen
WavLLM: Towards Robust and Adaptive Speech Large Language Model (2024.findings-emnlp)
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Shujie Hu, Long Zhou, Shujie Liu, Sanyuan Chen, Lingwei Meng, Hongkun Hao, Jing Pan, Xunying Liu, Jinyu Li, Sunit Sivasankaran, Linquan Liu, Furu Wei
| Challenge: | Recent advances in large language models (LLMs) have expanded their scope to encompass multimodal functions. |
| Approach: | They propose a robust and adaptive speech large language model with dual encoders . they validate the model on universal speech benchmarks and apply it to specialized speech-question-answer datasets based on a CoT approach . |
| Outcome: | The proposed model achieves state-of-the-art performance across a range of speech tasks on the same model size. |
“Are Your Explanations Reliable?” Investigating the Stability of LIME in Explaining Text Classifiers by Marrying XAI and Adversarial Attack (2023.emnlp-main)
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| Challenge: | Extensive experiments on text datasets demonstrate that XAIFooler significantly outperforms all baselines by large margins in its ability to manipulate LIME’s explanations with high semantic preservability. |
| Approach: | They propose to use LIME to establish a baseline and then propose an algorithm to perturb text inputs and manipulate explanations. |
| Outcome: | The proposed algorithm outperforms baselines on text datasets and achieves high semantic preservability. |
Autoregressive Speech Synthesis without Vector Quantization (2025.acl-long)
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Lingwei Meng, Long Zhou, Shujie Liu, Sanyuan Chen, Bing Han, Shujie Hu, Yanqing Liu, Jinyu Li, Sheng Zhao, Xixin Wu, Helen M. Meng, Furu Wei
| Challenge: | MELLE is a novel language modeling approach for text-to-speech synthesis that generates continuous tokens from text . authors demonstrate that it reduces the need for vector quantization and improves model robustness . |
| Approach: | They propose to autoregressively generate continuous mel-spectrogram frames directly from text condition, bypassing vector quantization. |
| Outcome: | The proposed model achieves superior performance across multiple metrics and is more streamlined. |
Structure-aware Propagation Generation with Large Language Models for Fake News Detection (2025.findings-emnlp)
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| Challenge: | propagation-based methods for fake news detection often lack structural data . authors propose a structure-aware synthetic propagation enhanced detection framework . |
| Approach: | They propose a structure-aware synthetic propagation enhanced detection framework to capture real-world propagation. |
| Outcome: | The proposed framework captures structural dynamics from real propagation, while ignoring structural patterns. |
Why LoRA Fails to Forget: Regularized Low-Rank Adaptation Against Backdoors in Language Models (2026.findings-acl)
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| Challenge: | Low-Rank Adaptation (LoRA) is widely used for parameter-efficient fine-tuning of large language models, but is ineffective at removing backdoor behaviors from poisoned pretrained models when fine-timing on clean datasets. |
| Approach: | They propose a low-rank Adaptation method which increases spectral strength and corrects alignment through clean-strengthened regularization and trigger-insensitive constraints. |
| Outcome: | The proposed method significantly reduces attack success rates while maintaining clean accuracy. |