Papers by Yuhan Wu
CAMEC: Complexity-Aware Multi-Expert Collaboration for Reliable Chinese Medical Question Answering (2026.acl-long)
Copied to clipboard
| Challenge: | Large language models are promising for medical question answering in china, but remain unreliable due to hallucinations, weak factual grounding and difficulty handling clinically complex cases. |
| Approach: | They propose a framework that combines hierarchical medical adaptation with complexity-aware expert routing for reliable Chinese medical QA. |
| Outcome: | The proposed framework outperforms strong general and medical LLM baselines on four Chinese medical benchmarks. |
Forest Before Trees: Latent Superposition for Efficient Visual Reasoning (2026.acl-long)
Copied to clipboard
| Challenge: | Recent latent reasoning methods suffer from a bandwidth bottleneck . explicit textual rationales suffer from premature semantic collapse . |
| Approach: | They propose a new paradigm that reformulates visual deduction via Dynamic Windowed Alignment Learning. |
| Outcome: | The proposed paradigm achieves state-of-the-art performance among latent reasoning methods surpassing the strong baseline Monet by 5.03% on average. |
Fortify the Shortest Stave in Attention: Enhancing Context Awareness of Large Language Models for Effective Tool Use (2024.acl-long)
Copied to clipboard
| Challenge: | In this paper, we demonstrate that an inherent waveform pattern in the attention allocation of large language models significantly affects their performance in tasks demanding a high degree of context awareness. |
| Approach: | They propose a method that compensates an attention trough with an attention peak by a process to enhance the model's awareness to various contextual positions. |
| Outcome: | The proposed method improves the performance of a 7B model on the largest tool-use benchmark, comparable to that of GPT-4. |
Bootstrapping Code Translation with Weighted Multilanguage Exploration (2026.acl-long)
Copied to clipboard
| Challenge: | Existing methods to improve code translation depend on abundant parallel code of high quality, which may not always be available. |
| Approach: | They propose a method that leverages functional invariance and cross-lingual portability of test suites to serve as universal verification oracles for multilingual reinforcement learning. |
| Outcome: | The proposed method leverages functional invariance and cross-lingual portability of test suites to serve as universal verification oracles for multilingual reinforcement learning (RL) training. |
Beyond Transcription: Unified Audio Schema for Perception-Aware AudioLLMs (2026.findings-acl)
Copied to clipboard
Linhao Zhang, Yuhan Song, Aiwei Liu, Chuhan Wu, Sijun Zhang, Wei Jia, Yuan Liu, Houfeng Wang, Zhou Xiao
| Challenge: | Recent Audio Large Language Models (AudioLLMs) excel at reasoning tasks, but struggle at elementary auditory perception. |
| Approach: | They propose a framework that organizes audio information into three explicit components in a unified JSON format. |
| Outcome: | The proposed framework boosts fine-grained perception by 10.9% on MMSU over state-of-the-art models while preserving robust reasoning capabilities. |
Detecting AI-Generated Video: A Vision–Language Dual-View Survey (2026.findings-acl)
Copied to clipboard
| Challenge: | realism of AI-generated Videos (AIGC-V) rendering artifact-centric detection insufficient, authors argue . a vision–language dual-view taxonomy is proposed to systematize this rapidly evolving field . |
| Approach: | They propose a Vision–Language Dual-View taxonomy to systematize AIGC-V detection . they propose realism of AI-generated Videos is rendering traditional inspection insufficient . |
| Outcome: | The proposed model aims to show that the existing methods are consistent with real-world facts. |
Dataflow-Guided Retrieval Augmentation for Repository-Level Code Completion (2024.acl-long)
Copied to clipboard
| Challenge: | Existing methods to generate correct code completions in private repositories are insufficiently relevant. |
| Approach: | They propose a dataflow-guided retrieval augmentation approach for repository-level code completion . they parses a private repository into code entities and establishes their relations through an extended dataflow analysis . |
| Outcome: | The proposed method improves code exact match and identifier F1-score by 3.43% compared to the state-of-the-art approach. |
EmoAgent: Assessing and Safeguarding Human-AI Interaction for Mental Health Safety (2025.emnlp-main)
Copied to clipboard
Jiahao Qiu, Yinghui He, Xinzhe Juan, Yimin Wang, Yuhan Liu, Zixin Yao, Yue Wu, Xun Jiang, Ling Yang, Mengdi Wang
| Challenge: | EmoAgent evaluates and mitigates mental health hazards in human-AI interactions, especially for vulnerable human users with psychological disorders. |
| Approach: | EmoAgent is a multi-agent AI framework designed to evaluate and mitigate mental health hazards in human-AI interactions. |
| Outcome: | EmoAgent evaluates and mitigates mental health hazards in human-AI interactions. |
Deputy: Accelerating Large Language Model Inference with Dynamic Low-Rank Substitution (2026.findings-acl)
Copied to clipboard
| Challenge: | Existing dynamic schemes such as early-exit and layer-drop reduce FLOPs but break batch processing or introduce KV-cache inconsistency. |
| Approach: | They propose a dynamic low-rank substitution framework that employs a lightweight decision module at each layer to dynamically determine the execution branch for different tokens. |
| Outcome: | The proposed model reduces computation by approximately 40% compared to the original dense model while outperforming existing baseline methods. |
Large Language Models Badly Generalize across Option Length, Problem Types, and Irrelevant Noun Replacements (2025.emnlp-main)
Copied to clipboard
| Challenge: | Existing benchmarks have exposed patterns and may not truly assess generalization ability of Large Language Models (LLMs). |
| Approach: | They propose a “Generalization Stress Test” to assess Large Language Models’ generalization ability under slight and controlled perturbations, including option length, problem types, and irrelevant noun replacements. |
| Outcome: | The proposed test shows that LLMs exhibit severe accuracy drops and unexpected biases when faced with minor but content-preserving modifications. |
RoleLLM: Benchmarking, Eliciting, and Enhancing Role-Playing Abilities of Large Language Models (2024.findings-acl)
Copied to clipboard
Noah Wang, Z.y. Peng, Haoran Que, Jiaheng Liu, Wangchunshu Zhou, Yuhan Wu, Hongcheng Guo, Ruitong Gan, Zehao Ni, Jian Yang, Man Zhang, Zhaoxiang Zhang, Wanli Ouyang, Ke Xu, Wenhao Huang, Jie Fu, Junran Peng
| Challenge: | Large Language Models (LLMs) have paved the way for complex tasks such as role-playing. |
| Approach: | They propose a framework to benchmark, elicit, and enhance role-playing abilities in Large Language Models. |
| Outcome: | The proposed framework improves role-playing abilities with 168,093 samples. |
DiaASQ: A Benchmark of Conversational Aspect-based Sentiment Quadruple Analysis (2023.findings-acl)
Copied to clipboard
Bobo Li, Hao Fei, Fei Li, Yuhan Wu, Jinsong Zhang, Shengqiong Wu, Jingye Li, Yijiang Liu, Lizi Liao, Tat-Seng Chua, Donghong Ji
| Challenge: | a new task of conversational aspect-based sentiment analysis (DiaASQ) is designed to detect the quadruple of target-aspect-opinion-sentiment in a dialogue. |
| Approach: | They propose a task of conversational aspect-based sentiment quadruple analysis to detect the quadrangle of target-aspect-opinion-sentiment in a dialogue. |
| Outcome: | The proposed task is based on a high-quality dataset in Chinese and English . it improves the end-to-end quadruple prediction and integrates rich feature representations . |
GlossaGen: Making Academic Translation Smarter with Glossing (2026.findings-acl)
Copied to clipboard
| Challenge: | Existing machine translation systems obscure or mistranslate key terminology, while paraphrasing aimed at lay readers often oversimplifies it, hindering their ability to master domain-specific technical vocabulary. |
| Approach: | They propose a task which produces translations dynamically adapted to a reader’s academic proficiency, or level, and a framework to address this challenge. |
| Outcome: | The proposed framework achieves higher scores than baselines on a synthesized benchmark and human evaluations. |