Papers by Yuhao Du
S2S-Arena: Evaluating Paralinguistic Instruction Following in Speech-to-Speech Models (2026.acl-long)
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Feng Jiang, Zhiyu Lin, Yiyang Liu, Liumeng Xue, Fan Bu, Yuhao Du, Xiangying Chen, Benyou Wang, Haizhou Li
| Challenge: | Existing benchmarks rely heavily on text-based evaluation and largely ignore paralinguistic cues such as prosody, emotion, and speaker traits. |
| Approach: | They propose a speech-native benchmark for evaluating instruction-following S2S models with explicit assessment of both semantic understanding and paralinguistic expression. |
| Outcome: | The proposed system enables more natural, robust, and human-aligned speech agents. |
Add-One-In: Incremental Sample Selection for Large Language Models via a Choice-Based Greedy Paradigm (2025.emnlp-main)
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| Challenge: | Existing studies focus on individual quality and do not assess the value of training data. |
| Approach: | They propose a choice-based sample selection framework that evaluates sample quality . they use LLMs to evaluate the value of each option during the selection process . |
| Outcome: | The proposed model outperforms the full dataset and recent studies on a larger medical dataset. |
Atoxia: Red-teaming Large Language Models with Target Toxic Answers (2025.findings-naacl)
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| Challenge: | Large language models (LLMs) are still vulnerable to generation safety vulnerabilities. |
| Approach: | They propose a method that A**tacks LLMs with target "toxi" given a particular harmful answer, the method generates a user query and a misleading answer opening to examine the internal defects of a given LLM. |
| Outcome: | The proposed method detects safety risks in open-source models and state-of-the-art models such as GPT-4o. |
Bridging Relevance and Reasoning: Rationale Distillation in Retrieval-Augmented Generation (2025.findings-acl)
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Pengyue Jia, Derong Xu, Xiaopeng Li, Zhaocheng Du, Xiangyang Li, Yichao Wang, Yuhao Wang, Qidong Liu, Maolin Wang, Huifeng Guo, Ruiming Tang, Xiangyu Zhao
| Challenge: | Existing approaches to rerank and align documents based on reasoning capabilities of large language models (LLMs) . prior work shows that LLMs have exceptional reasoning and text generation capabilities . |
| Approach: | They propose a rationale extraction method that leverages reasoning capabilities of large language models to extract the rationales necessary for answering a query. |
| Outcome: | The proposed method is compared with baseline methods on two tasks across three datasets. |
CodeArena: A Collective Evaluation Platform for LLM Code Generation (2025.acl-demo)
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Mingzhe Du, Anh Tuan Luu, Bin Ji, Xiaobao Wu, Yuhao Qing, Dong Huang, Terry Yue Zhuo, Qian Liu, See-Kiong Ng
| Challenge: | Large Language Models (LLMs) have reshaped code generation, but persistent challenges impede accurate assessment. |
| Approach: | They propose an online evaluation framework tailored for large language models to assess their coding capabilities. |
| Outcome: | a new evaluation framework for large language models (LLMs) provides unbiased, unbiased evaluations and open access to solutions and test cases. |
Second Language (Arabic) Acquisition of LLMs via Progressive Vocabulary Expansion (2025.acl-long)
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Jianqing Zhu, Huang Huang, Zhihang Lin, Juhao Liang, Zhengyang Tang, Khalid Almubarak, Mosen Alharthi, Bang An, Juncai He, Xiangbo Wu, Fei Yu, Junying Chen, Ma Zhuoheng, Yuhao Du, He Zhang, Saied Alshahrani, Emad A. Alghamdi, Lian Zhang, Ruoyu Sun, Haizhou Li, Benyou Wang, Jinchao Xu
| Challenge: | In the evolving landscape of large language models, the predominant focus has been on English and Chinese. |
| Approach: | They propose to utilize Arabic-specific vocabulary in the tokenizer to accelerate decoding. |
| Outcome: | The proposed model achieves decent performance comparable to the best Arabic LLMs across various Arabic benchmarks. |
Self-Instructed Derived Prompt Generation Meets In-Context Learning: Unlocking New Potential of Black-Box LLMs (2025.acl-long)
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| Challenge: | Existing prompt refinement methods suffer from semantic inconsistencies and fail to maintain users’ real intent. |
| Approach: | They propose a self-instructed in-context learning framework that generates reliable derived prompts while keeping semantic consistency with original prompts. |
| Outcome: | The proposed framework generates better derived prompts and significantly enhances LLMs’ ability to deliver more effective responses. |
HER: Human-like Reasoning and Reinforcement Learning for LLM Role-playing (2026.findings-acl)
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Chengyu Du, Xintao Wang, Aili Chen, Weiyuan Li, Rui Xu, Junteng Liu, Zishan Huang, Rong Tian, Zijun Sun, Yuhao Li, Liheng Feng, Deming Ding, Pengyu Zhao, Yanghua Xiao
| Challenge: | Existing models for LLM role-playing lack high-quality datasets with explicit reasoning traces and reliable reward signals aligned with human preferences. |
| Approach: | They propose a unified framework for cognitive-level persona simulation that strictly distinguishes characters’ first-person thinking processes from LLMs’ third-person reasoning. |
| Outcome: | The proposed framework outperforms the Qwen3-32B baseline model and achieves a 30.26% and 14.97% performance on the minimax benchmarks. |