Papers by Ruocheng Wang
Noise-Robust Fine-Tuning of Pretrained Language Models via External Guidance (2023.findings-emnlp)
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| Challenge: | Pretrained Language Models (PLMs) are advanced but data labels are noisy due to the complex annotation process. |
| Approach: | They propose a framework for fine-tuning PLMs using noisy labels that incorporates guidance from Large Language Models like ChatGPT. |
| Outcome: | Experiments on synthetic and real-world noisy datasets show that the proposed framework outperforms the state-of-the-art framework. |
Language-Mediated, Object-Centric Representation Learning (2021.findings-acl)
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| Challenge: | Recent work has studied the problem of unsupervised object representation learning, though without language. |
| Approach: | They propose language-mediated, Objectcentric Representation Learning (LORL) a paradigm for learning disentangled, objectcentric scene representations from vision and language. |
| Outcome: | The proposed paradigm improves performance of unsupervised object discovery algorithms on two datasets using language. |
Stepwise Reasoning Disruption Attack of LLMs (2025.acl-long)
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Jingyu Peng, Maolin Wang, Xiangyu Zhao, Kai Zhang, Wanyu Wang, Pengyue Jia, Qidong Liu, Ruocheng Guo, Qi Liu
| Challenge: | Existing attacks on LLM reasoning are constrained by specific settings or lack of imperceptibility, limiting their feasibility and generalizability. |
| Approach: | They propose a stepwise rEasoning error disruption attack that subtly injects errors into prior reasoning steps to mislead the model into producing incorrect subsequent reasoning and final answers. |
| Outcome: | The proposed attack is compatible with zero-shot and few-shot settings, maintains the natural reasoning flow, and ensures covert execution without modification of the instruction. |