Papers by Yuhao Yang
RecLM: Recommendation Instruction Tuning (2025.acl-long)
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| Challenge: | Modern recommender systems aim to understand user-item relationships through past interactions, but their effectiveness is limited when handling sparse data or zero-shot scenarios. |
| Approach: | They propose a model-agnostic recommendation instruction-tuning paradigm that integrates large language models with collaborative filtering. |
| Outcome: | The proposed model-agnostic recommendation instruction-tuning paradigm improves performance across various settings and plug-and-play compatibility with state-of-the-art recommender systems. |
Breaking Down Power Barriers in On-Device Streaming ASR: Insights and Solutions (2025.naacl-industry)
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Yang Li, Yuan Shangguan, Yuhao Wang, Liangzhen Lai, Ernie Chang, Changsheng Zhao, Yangyang Shi, Vikas Chandra
| Challenge: | Streaming automatic speech recognition models use high power consumption to improve usability and accuracy. |
| Approach: | They propose to optimize on-device speech recognition models by adjusting component energy sensitivities based on their specific energy sensitities to reduce power consumption. |
| Outcome: | The proposed approach achieves up to 47% lower energy usage while preserving comparable model accuracy and improving real-time performance compared to leading methods. |
Enhancing Speech Large Language Models with Prompt-Aware Mixture of Audio Encoders (2025.emnlp-main)
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Weiqiao Shan, Yuang Li, Yuhao Zhang, Yingfeng Luo, Chen Xu, Xiaofeng Zhao, Long Meng, Yunfei Lu, Min Zhang, Hao Yang, Tong Xiao, JingBo Zhu
| Challenge: | Existing work on integrating audio encoders with large language models (LLMs) has focused on semantic understanding tasks, but different tasks may require distinct features that emphasize either semantic or acoustic aspects. |
| Approach: | They propose to use a prompt-aware mixture to enhance the Speech LLM that uses multiple audio encoders to extract different features based on the prompt. |
| Outcome: | The proposed approach outperforms all single-encoder Speech LLMs on ASR, speaker number verification, and AC tasks. |
SPARK: Strategic Policy-Aware Exploration via Dynamic Branching for Long-Horizon Agentic Learning (2026.acl-long)
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| Challenge: | Existing methods for training large language models waste computation budget on trivial steps while failing to guarantee sample quality. |
| Approach: | They propose a framework that selectively branches at critical decision states for resource-efficient exploration. |
| Outcome: | The proposed framework activates adaptive branching exploration at critical decision states to probe promising trajectories, thereby achieving precise resource allocation that prioritizes sampling quality over blind coverage. |
Traffic-R1: Reinforced LLMs Bring Human-Like Reasoning to Traffic Signal Control Systems (2026.acl-long)
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| Challenge: | Rapid urbanization and surging vehicle ownership intensify congestion . rapid urbanization drives crash rates, slow emergency response, and burden transit-poor communities . |
| Approach: | They introduce a 3B-parameter foundation model with human-like reasoning for Traffic signal control (TSC) they use reinforcement learning and network communication to convert LLM into a traffic-control model that operates like a human traffic agent. |
| Outcome: | The proposed model outperforms baselines and training-intensive RL controllers on a simulated traffic environment and reduces queue lengths by more than 5%. |
CTC-based Non-autoregressive Speech Translation (2023.acl-long)
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Chen Xu, Xiaoqian Liu, Xiaowen Liu, Qingxuan Sun, Yuhao Zhang, Murun Yang, Qianqian Dong, Tom Ko, Mingxuan Wang, Tong Xiao, Anxiang Ma, Jingbo Zhu
| Challenge: | End-to-end speech translation (E2E ST) and non-autoregressive (NAR) generation are promising in language and speech processing for their advantages of less error propagation and low latency. |
| Approach: | They develop a model that uses connectionist temporal classification to predict the source and target texts. |
| Outcome: | The proposed model achieves an average BLEU score of 29.5 with a speed-up of 5.67. |
Fundamental Capabilities of Large Language Models and their Applications in Domain Scenarios: A Survey (2024.acl-long)
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Jiawei Li, Yizhe Yang, Yu Bai, Xiaofeng Zhou, Yinghao Li, Huashan Sun, Yuhang Liu, Xingpeng Si, Yuhao Ye, Yixiao Wu, 林一冠 林一冠, Bin Xu, Ren Bowen, Chong Feng, Yang Gao, Heyan Huang
| Challenge: | Existing benchmark-based evaluations cannot accurately reflect the performance of real-world applications. |
| Approach: | They propose a reliable strategy for domains to choose more robust LLMs for real-world applications. |
| Outcome: | The proposed strategy addresses the challenges faced by domains to choose more robust LLMs for real-world applications. |
HAHE: Hierarchical Attention for Hyper-Relational Knowledge Graphs in Global and Local Level (2023.acl-long)
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Haoran Luo, Haihong E, Yuhao Yang, Yikai Guo, Mingzhi Sun, Tianyu Yao, Zichen Tang, Kaiyang Wan, Meina Song, Wei Lin
| Challenge: | Existing research on HKGs rarely models the graphical and sequential structure of HKG, limiting their representation. |
| Approach: | They propose a Hierarchical Attention model for HKG Embedding that includes global-level and local-level attention to model the graphical structure of HKGs. |
| Outcome: | The proposed model achieves state-of-the-art performance on HKG standard datasets and addresses the issue of HKG multi-position prediction for the first time. |
GraphAgent: Agentic Graph Language Assistant (2025.emnlp-main)
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| Challenge: | Real-world data combines structured and unstructured formats, capturing explicit relationships and implicit semantic interdependencies. |
| Approach: | They propose GraphAgent, an automated agent pipeline addressing both explicit and implicit graph-enhanced semantic dependencies for predictive and generative tasks. |
| Outcome: | Extensive experiments on diverse datasets validate GraphAgent’s effectiveness in graph-related predictive and text generative tasks. |
PSST: A Benchmark for Evaluation-driven Text Public-Speaking Style Transfer (2024.findings-emnlp)
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| Challenge: | Existing methods to transfer text style focus on sentence-level data, limiting performance . current LLMs struggle to generate public speaking texts that align with human preferences . |
| Approach: | They propose a task to transform official texts into public-speaking styles by analyzing real-world data. |
| Outcome: | The proposed task aims to transform public speaking texts into public-speaking styles . the proposed framework analyzes characteristics and identifies problems of stylized texts . |
Aria-UI: Visual Grounding for GUI Instructions (2025.findings-acl)
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| Challenge: | Using a multimodal model, GUI agents can ground from language instructions to target elements . relying on HTML or AXTree inputs is a challenge for GUI agents . |
| Approach: | They propose a large multimodal model specifically designed for GUI grounding that adopts a pure vision approach instead of auxiliary inputs. |
| Outcome: | The proposed model outperforms vision-only and AXTree-reliant models on offline and online agents. |
Enhancing Agentic Textual Graph Retrieval with Synthetic Stepwise Supervision (2026.acl-long)
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Ge Chang, Jinbo Su, Jiacheng Liu, Pengfei Yang, Yuhao Shang, Huiwen Zheng, Hongli Ma, Yan Liang, Yuanchun Li, Yunxin Liu
| Challenge: | Existing methods for integrating textual graphs with LLMs are limited by symbolic inference and high annotation costs. |
| Approach: | They propose a textual graph reasoning framework that integrates textual diagrams with large language models. |
| Outcome: | The proposed approach achieves 15.6% accuracy and 17.2% in F1 score on three common datasets. |