Papers by Zhijie Huang
Don’t Say No: Jailbreaking LLM by Suppressing Refusal (2025.findings-acl)
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| Challenge: | Large Language Models (LLMs) are vulnerable to "jailbreaking" attacks where crafted prompts manipulate them into producing toxic content. |
| Approach: | They propose to improve the target loss objective by combining a cosine decay schedule method with refusal suppression to achieve higher success rates. |
| Outcome: | The proposed approach outperforms baseline attacks and achieves state-of-the-art attack success rates. |
MT3: A Synergistic Multi-Task RL Framework for Specializing MLLMs in Text Image Machine Translation (2026.acl-long)
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Zhaopeng Feng, Yupu Liang, Shaosheng Cao, Jiayuan Su, Jiahan Ren, Zhijie Zhou, Wenxuan Huang, Jian Wu, Zuozhu Liu
| Challenge: | Text Image Machine Translation (TIMT) is a critical subfield of machine translation . it requires accurate optical character recognition, robust visual-text reasoning, and high-quality translation a challenge . |
| Approach: | They propose a multi-task optimization framework to specialize MLLMs into expert TIMT models. |
| Outcome: | The proposed model outperforms baselines on the latest in-domain MIT-10M benchmark. |
SIFT: Grounding LLM Reasoning in Contexts via Stickers (2025.findings-emnlp)
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| Challenge: | Using a new approach, we can improve the pass@1 accuracy of LLM reasoning in large language models. |
| Approach: | They propose a method that leverages increasing inference-time compute to ground LLM reasoning in contexts. |
| Outcome: | The proposed approach improves pass@1 accuracy of DeepSeek-R1 on AIME2024 from 78.33% to **85.67%** and that on Aime2025 from 69.8% to **77.33%**. |
SFMSS: Service Flow aware Medical Scenario Simulation for Conversational Data Generation (2025.findings-naacl)
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| Challenge: | Medical-specific Large Language Models (LLMs) have demonstrated impressive performance on medical-related exams and tasks. |
| Approach: | They propose a framework for medical conversational data generation that uses Authentic Seed Data to ensure quality of the data. |
| Outcome: | The proposed model outperforms all baselines and human evaluations, and aligns with human preferences and clinical demands. |
TESTEVAL: Benchmarking Large Language Models for Test Case Generation (2025.findings-naacl)
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Wenhan Wang, Chenyuan Yang, Zhijie Wang, Yuheng Huang, Zhaoyang Chu, Da Song, Lingming Zhang, An Ran Chen, Lei Ma
| Challenge: | Existing methods to generate test cases using large language models are limited in their ability to generate unit test cases. |
| Approach: | They propose a test case generation benchmark that uses large language models to generate unit test cases. |
| Outcome: | The proposed test case generation benchmarks compare LLMs with commercial and open-source LLM platforms and find that they lack the ability to comprehend program logic and execution paths. |
Fast Quiet-STaR: Thinking Without Thought Tokens (2025.findings-emnlp)
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| Challenge: | Large Language Models have achieved impressive performance across a range of tasks, but further gains require more than scaling up model sizes or training data. |
| Approach: | They propose a method that gradually reduces the number of thought tokens . this method allows models to internalize more abstract reasoning processes . |
| Outcome: | The proposed framework preserves the benefits of token-level reasoning while reducing computational cost. |
TPS-Bench: Evaluating AI Agents’ Tool Planning & Scheduling Abilities in Compounding Tasks (2026.acl-long)
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| Challenge: | Large language model (LLM) agents have demonstrated strong problem-solving competence across domains like research and coding. |
| Approach: | They propose to use a tool repository to analyze the ability of large language model agents to solve complex problems. |
| Outcome: | The proposed model outperforms open-source and closed-source models in task completion rate and efficiency. |