Papers by Ruocheng Guo
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. |
ToolPRMBench: Evaluating and Advancing Process Reward Models for Tool-using Agents (2026.findings-acl)
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| Challenge: | Reward-guided search methods have shown potential in enhancing tool-using agents . however, there is a lack of reliable evaluation benchmarks for PRMs in tool-use settings . |
| Approach: | They propose a large-scale benchmark specifically designed to evaluate PRMs for tool-using agents. |
| Outcome: | The proposed benchmark shows that tool reward models perform better in tool-using environments. |
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. |
RIMRULE: Improving Tool-Using Language Agents via MDL-Guided Rule Learning (2026.acl-long)
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Xiang Gao, Yuguang Yao, Qi Zhang, Kaiwen Dong, Avinash Baidya, Ruocheng Guo, Hilaf Hasson, Kamalika Das
| Challenge: | Large language models (LLMs) struggle to use tools reliably in domain-specific settings. |
| Approach: | They propose a neuro-symbolic approach to adapt large language models to task-specific tools . they propose reusable rules that are distilled from failure traces and injected into the prompt . |
| Outcome: | Experiments show that the proposed approach outperforms prompting-based adaptation methods and complements finetuning. |