Papers by Yuhang He
ScaleLLM: A Resource-Frugal LLM Serving Framework by Optimizing End-to-End Efficiency (2024.emnlp-industry)
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Yuhang Yao, Han Jin, Alay Shah, Shanshan Han, Zijian Hu, Dimitris Stripelis, Yide Ran, Zhaozhuo Xu, Salman Avestimehr, Chaoyang He
| Challenge: | Large language models (LLMs) are widely used in commercial applications . low latency is crucial due to system latency, query concurrency, and computational resources constraints. |
| Approach: | They propose a system that can be resource-efficiently served by addressing bottlenecks beyond LLM inference . they propose 4.3 speed up over vLLM and 1.5 higher throughput . |
| Outcome: | The proposed system outperforms state-of-the-arts with 1.5 higher throughput . it achieves 4.3 speed up with 64 concurrent requests on Mixtral 8x7B . |
Graph-GRPO: Stabilizing Multi-Agent Topology Learning via Group Relative Policy Optimization (2026.findings-acl)
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Yueyang Cang, Xiaoteng Zhang, Erlu Zhao, Zehua Ji, Yuhang Liu, Yuchen He, Zhiyuan Ning, Chen Yijun, Wenge Que, Li Shi
| Challenge: | Recent approaches to optimize communication topology rely on single-sample policy gradients with absolute rewards. |
| Approach: | They propose a topology optimization framework that integrates Group Relative Policy Optimization. |
| Outcome: | The proposed topology optimization framework outperforms state-of-the-art methods on reasoning and code generation benchmarks. |
From Outcome to Process: Optimizing MoE Load Balancing with MCTS (2026.findings-acl)
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Wenjun Ke, Hengyuan Xu, Ziyu Shang, Yao He, Jiahao Wang, Zijie Xu, Peng Wang, Yuhang Lou, Jiajun Liu
| Challenge: | Existing balancing strategies focus on constraining the final distribution of expert usage, but overlook the routing decisions made at each layer. |
| Approach: | They propose a three-stage framework that leverages process-level rewards to guide balanced expert routing. |
| Outcome: | Extensive experiments show that LayerMoE improves the performance of state-of-the-art LoRA-MoA baselines, yielding an average accuracy gain of 1.39%. |
RepoDebug: Repository-Level Multi-Task and Multi-Language Debugging Evaluation of Large Language Models (2025.findings-emnlp)
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Jingjing Liu, Zeming Liu, Zihao Cheng, Mengliang He, Xiaoming Shi, Yuhang Guo, Xiangrong Zhu, Yuanfang Guo, Yunhong Wang, Haifeng Wang
| Challenge: | Large Language Models (LLMs) have exhibited significant proficiency in code debugging, especially in automatic program repair. |
| Approach: | They propose a repository-level code debugging dataset with 22 subtypes of errors that supports 8 commonly used programming languages and 3 debug tasks. |
| Outcome: | The proposed dataset supports 8 commonly used programming languages and 3 debugging tasks. |
TensorOpera Router: A Multi-Model Router for Efficient LLM Inference (2024.emnlp-industry)
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Dimitris Stripelis, Zhaozhuo Xu, Zijian Hu, Alay Shah, Han Jin, Yuhang Yao, Jipeng Zhang, Tong Zhang, Salman Avestimehr, Chaoyang He
| Challenge: | Large Language Models (LLMs) have demonstrated remarkable performance across a diverse set of domain-specific tasks. |
| Approach: | They propose a non-monolithic LLM querying system that seamlessly integrates various LLM experts into a single query interface and dynamically routes incoming queries to the most high-performant expert based on query’s requirements. |
| Outcome: | The proposed model improves query efficiency by 40% and costs by 30% while maintaining or enhancing model performance by 10%. |
MinerU2.5: A Decoupled Vision-Language Model for Efficient High-Resolution Document Parsing (2026.acl-industry)
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Junbo Niu, Zheng Liu, Zhuangcheng Gu, Bin Wang, Linke Ouyang, Zhiyuan Zhao, Tao Chu, Tianyao He, Fan Wu, Qintong Zhang, Zhenjiang Jin, Guang Liang, Rui Zhang, Wenzheng Zhang, Yuan Qu, Zhifei Ren, Yuefeng Sun, Zirui Tang, Boyu Niu, Yuanhong Zheng, Dongsheng Ma, Ziyang Miao, Hejun Dong, Siyi Qian, Junyuan Zhang, Fangdong Wang, Jingzhou Chen, Xiaomeng Zhao, Liqun Wei, Wei Li, Shasha Wang, RuiLiang Xu, Yuanyuan Cao, Lu Chen, Qianqian Wu, Huaiyu Gu, Lindong Lu, Dechen Lin, null Shenguanlin, Xuanhe Zhou, Linfeng Zhang, Yuhang Zang, Xiaoyi Dong, Jiaqi Wang, Bo Zhang, Lei Bai, Pei Chu, Weijia Li, Jiang Wu, Lijun Wu, Zhenxiang Li, Guangyu Wang, Zhongying Tu, Chao Xu, Kai Chen, Bowen Zhou, Dahua Lin, Wentao Zhang, Conghui He
| Challenge: | Document images are characterized by higher resolutions, denser content, and more complex structural layouts. |
| Approach: | They propose a 1.2B-parameter document parsing vision-language model that decouples layout analysis from local content recognition. |
| Outcome: | The proposed model surpasses general-purpose and domain-specific models on multiple benchmarks while maintaining significantly lower computational overhead. |
BC-Prover: Backward Chaining Prover for Formal Theorem Proving (2024.emnlp-main)
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| Challenge: | Existing methods for interactive theorem proving in formal logic lack robustness and robustness. |
| Approach: | They propose a backward chaining framework guided by pseudo steps for proofstep generation that prioritizes pseudo steps. |
| Outcome: | The proposed framework improves on the miniF2F benchmark. |
A Generative Model for End-to-End Argument Mining with Reconstructed Positional Encoding and Constrained Pointer Mechanism (2022.emnlp-main)
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| Challenge: | Argument mining (AM) is a challenging task as it requires recognizing complex argumentation structures involving multiple subtasks. |
| Approach: | They propose a generative framework where expected outputs of AM are framed as a simple target sequence. |
| Outcome: | The proposed framework achieves state-of-the-art on two AM benchmarks. |
RealHiTBench: A Comprehensive Realistic Hierarchical Table Benchmark for Evaluating LLM-Based Table Analysis (2025.findings-acl)
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Pengzuo Wu, Yuhang Yang, Guangcheng Zhu, Chao Ye, Hong Gu, Xu Lu, Ruixuan Xiao, Bowen Bao, Yijing He, Liangyu Zha, Wentao Ye, Junbo Zhao, Haobo Wang
| Challenge: | Existing benchmarks for large language models focus on simple, flat table structures. |
| Approach: | They propose a benchmark to evaluate the performance of both Large Language Models and Multimodal LLMs across a variety of input formats for complex tabular data, including LaTeX, HTML, and PNG. |
| Outcome: | The proposed benchmark evaluates the performance of LLMs and Multimodal LLM models across a variety of input formats for complex tabular data, including LaTeX, HTML, and PNG. |
Decomposing Argumentative Essay Generation via Dialectical Planning of Complex Reasoning (2024.findings-acl)
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| Challenge: | Argumentative Essay Generation (AEG) is a challenging task in computational argumentation, where detailed logical reasoning and effective rhetorical skills are essential. |
| Approach: | They propose an argumentative planning strategy for prompting large language models to generate high-quality essays by sketch planning and dialectical planning. |
| Outcome: | The proposed method generates more dialectical and persuasive essays with higher diversity compared to baselines. |
Mementos: A Comprehensive Benchmark for Multimodal Large Language Model Reasoning over Image Sequences (2024.acl-long)
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Xiyao Wang, Yuhang Zhou, Xiaoyu Liu, Hongjin Lu, Yuancheng Xu, Feihong He, Jaehong Yoon, Taixi Lu, Fuxiao Liu, Gedas Bertasius, Mohit Bansal, Huaxiu Yao, Furong Huang
| Challenge: | Multimodal Large Language Models (MLLMs) have demonstrated proficiency in handling a variety of visual-language tasks, but their ability to extrapolate from image sequences has been less investigated. |
| Approach: | They propose a new benchmark to assess MLLMs’ sequential image reasoning abilities. |
| Outcome: | The proposed benchmark features 4,761 diverse image sequences with varying lengths. |
The CRECIL Corpus: a New Dataset for Extraction of Relations between Characters in Chinese Multi-party Dialogues (2022.lrec-1)
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Yuru Jiang, Yang Xu, Yuhang Zhan, Weikai He, Yilin Wang, Zixuan Xi, Meiyun Wang, Xinyu Li, Yu Li, Yanchao Yu
| Challenge: | Existing datasets focus on relation extraction between two entities in one sentence, and some focus on cross-sentence relationships. |
| Approach: | They propose to use a Chinese multi-party dialogue dataset for automatic extraction of dialogue-based character relationships. |
| Outcome: | The proposed dataset extracts relationships between 140 entities on the CRECIL corpus and another existing relation extraction corpus. |
SuLoRA: Subspace Low-Rank Adaptation for Parameter-Efficient Fine-Tuning (2025.findings-acl)
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| Challenge: | Existing methods for fine-tuning large language models (LLMs) introduce parameter interference, leading to a gap in generalization performance for specific tasks compared to full fine-uning. |
| Approach: | They propose a parameter-separated low-rank adapter to account for task differences by decomposing LoRA’s parameter matrix into multiple independent subspaces and assigning them differentially to distinct tasks. |
| Outcome: | The proposed method outperforms LoRA in trainable parameter efficiency and overall model performance on various NLP tasks. |
RiTTA: Modeling Event Relations in Text-to-Audio Generation (2025.emnlp-main)
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| Challenge: | Existing text-to-audio (TTA) generation methods have not explored audio event relation modeling, nor proposed any new framework to enhance this capability. |
| Approach: | They propose a comprehensive relation corpus covering all potential relations in real-world scenarios and a new audio event corpus encompassing commonly heard audios. |
| Outcome: | The proposed framework improves existing models’ relation modeling capability with negligible extra parameters. |
Are LLMs Rational Investors? A Study on the Financial Bias in LLMs (2025.findings-acl)
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Yuhang Zhou, Yuchen Ni, Zhiheng Xi, Zhangyue Yin, Yu He, Gan Yunhui, Xiang Liu, Zhang Jian, Sen Liu, Xipeng Qiu, Yixin Cao, Guangnan Ye, Hongfeng Chai
| Challenge: | Existing studies on biases within specific domains, such as finance, remain limited. |
| Approach: | They propose a framework to detect, detect, analyze and mitigate financial biases in large language models. |
| Outcome: | The proposed framework reduces bias by 68% for the most biased model, according to key metrics. |
R3-NL2GQL: A Model Coordination and Knowledge Graph Alignment Approach for NL2GQL (2024.findings-emnlp)
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Yuhang Zhou, Yu He, Siyu Tian, Yuchen Ni, Zhangyue Yin, Xiang Liu, Chuanjun Ji, Sen Liu, Xipeng Qiu, Guangnan Ye, Hongfeng Chai
| Challenge: | Adapting existing approaches for converting natural language to SQL encounters hurdles due to distinct nature of GQL compared to SQL. |
| Approach: | They propose a method that integrates both small and large Foundation Models for ranking, rewriting, and refining tasks. |
| Outcome: | The proposed approach integrates both small and large Foundation Models for ranking, rewriting, and refining tasks while capitalizing on the superior generalization and query generation prowess of larger models for the final transformation of natural language queries into GQL formats. |