Papers by Hengrui Chen
Cross-Lingual Multi-Hop Knowledge Editing (2024.findings-emnlp)
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| Challenge: | Prior work on knowledge editing in monolingual settings focused on a single language, but there are significant gaps in performance between the two settings. |
| Approach: | They propose a cross-lingual multi-hop knowledge editing paradigm for measuring and analyzing the performance of various SoTA knowledge editing techniques in a multilingual setup. |
| Outcome: | The proposed system improves on previous methods in a cross-lingual setting and in English. |
The Efficiency vs. Accuracy Trade-off: Optimizing RAG-Enhanced LLM Recommender Systems Using Multi-Head Early Exit (2025.acl-long)
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Huixue Zhou, Hengrui Gu, Zaifu Zhan, Xi Liu, Kaixiong Zhou, Yongkang Xiao, Mingfu Liang, Srinivas Prasad Govindan, Piyush Chawla, Jiyan Yang, Xiangfei Meng, Huayu Li, Buyun Zhang, Liang Luo, Wen-Yen Chen, Yiping Han, Bo Long, Rui Zhang, Tianlong Chen
| Challenge: | Existing frameworks for Large Language Models (LLMs) for Click-Through Rate prediction require a careful balance between computational efficiency and predictive accuracy. |
| Approach: | They propose a framework that integrates Retrieval-Augmented Generation with a novel multi-head early exit architecture to address both challenges. |
| Outcome: | The proposed framework reduces retrieval time while maintaining high model performance. |
Beyond the Singular: Revealing the Value of Multiple Generations in Benchmark Evaluation (2026.findings-acl)
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| Challenge: | Existing evaluation methods for large language models overlook the inherent randomness of LLMs. |
| Approach: | They propose a hierarchical statistical model that incorporates both benchmark characteristics and LLM randomness to provide a more comprehensive representation of benchmarking process. |
| Outcome: | The proposed model improves the accuracy of estimating the benchmark score and reduces variance. |
Layer-Level Self-Exposure and Patch: Affirmative Token Mitigation for Jailbreak Attack Defense (2025.naacl-long)
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Yang Ouyang, Hengrui Gu, Shuhang Lin, Wenyue Hua, Jie Peng, Bhavya Kailkhura, Meijun Gao, Tianlong Chen, Kaixiong Zhou
| Challenge: | Existing methods to defend against jailbreak attacks exploit vulnerabilities to elicit unintended or harmful outputs. |
| Approach: | They propose a method to defend against jailbreak attacks by patching specific layers within large language models through self-augmented datasets. |
| Outcome: | The proposed approach reduces harmfulness and attack success rate of jailbreak attacks without compromising utility for benign queries compared to previous methods. |
Good Reasoning Makes Good Demonstrations: Implicit Reasoning Quality Supervision via In-Context Reinforcement Learning (2026.findings-acl)
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| Challenge: | Reinforcement Learning with Verifiable Rewards (RLVR) improves reasoning in large language models but treats all correct solutions equally, potentially reinforcing flawed traces that arrive at correct answers by chance. |
| Approach: | They propose a method that reweights rewards by a factor approximately proportional to Evidence Gain and assigns higher weights to high-quality traces without requiring costly computation. |
| Outcome: | Experiments on mathematical reasoning benchmarks show that Reinforcement Learning with Verifiable Rewards (RLVR) improves reasoning in large language models but treats all correct solutions equally. |