Papers by Yuhang Guo
Outlier Suppression+: Accurate quantization of large language models by equivalent and effective shifting and scaling (2023.emnlp-main)
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| Challenge: | asymmetric outliers in transformer language models are a challenge for post-training quantization . we propose a framework for outlier suppression that can be seamlessly migrated into subsequent modules . |
| Approach: | They propose a framework for post-training quantization that includes the channel-wise shifting and scaling for concentration. |
| Outcome: | The proposed framework can be migrated into subsequent modules while maintaining equivalence. |
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. |
FAME: Towards Factual Multi-Task Model Editing (2024.emnlp-main)
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| Challenge: | Large language models embed extensive knowledge and perform exceptionally well across tasks. outdated knowledge or factual errors within LLMs can lead to misleading or incorrect responses. |
| Approach: | They propose to use a dataset to enhance the practicality of model editing to correct inaccurate information within LLMs. |
| Outcome: | The proposed method performs excellently across tasks and scenarios, confirming its practicality. |
TED-EL: A Corpus for Speech Entity Linking (2024.lrec-main)
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| Challenge: | Current entity linking tasks rely on textual information, but entities usually exist in textual, audio, and visual contexts in real-world data such as social media and video websites. |
| Approach: | They propose a speech entity linking task to recognize mentions from speech and link them to entities in knowledge bases. |
| Outcome: | The proposed model outperforms the existing models on the TED-EL dataset, scoring an F1 score of 60.68%. |
TransBench: Breaking Barriers for Transferable Graphical User Interface Agents in Dynamic Digital Environments (2025.findings-acl)
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Yuheng Lu, Qian Yu, Hongru Wang, Zeming Liu, Wei Su, Yanping Liu, Yuhang Guo, Maocheng Liang, Yunhong Wang, Haifeng Wang
| Challenge: | Existing GUI agents struggle to adapt to dynamic and interconnected nature of real-world digital environments, authors show . |
| Approach: | They propose a benchmark to evaluate the transferability of GUI agents across three key dimensions . transBench includes 15 app categories with diverse functionalities . |
| Outcome: | The proposed benchmark shows that existing GUI agents struggle to adapt to dynamic, interconnected environments. |
ToolSpectrum: Towards Personalized Tool Utilization for Large Language Models (2025.findings-acl)
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| Challenge: | Existing approaches focus on functional tool selection following user instructions while overlooking the critical role of context-aware personalization in tool selection. |
| Approach: | They propose a benchmark to evaluate LLMs’ capabilities in personalized tool utilization. |
| Outcome: | The proposed benchmark evaluates LLMs' capabilities in personalized tool utilization. |
Automatic Evaluate Dialogue Appropriateness by Using Dialogue Act (2023.findings-emnlp)
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| Challenge: | Existing evaluations of dialogue quality rely on human judgments, which are time-consuming, labor-intensive, prone to biases, and lacking objectivity. |
| Approach: | They propose a method that utilizes the underlying patterns of dialogue act transitions to evaluate the appropriateness of chatbot responses. |
| Outcome: | The proposed method proves that human judgments are time-consuming, labor-intensive, and lacking objectivity. |
Exploring In-Image Machine Translation with Real-World Background (2025.findings-acl)
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| Challenge: | Existing models for IIMT focus on simplified scenarios, which is far from reality and impractical for applications in the real world. |
| Approach: | They propose a model that separates the background and text-image from the source image and performs translation on the text- image directly. |
| Outcome: | The proposed model improves translation quality and visual effect in complex scenarios . it separates background and text-image from source image and performs translation on the text- image directly . |
PRIM: Towards Practical In-Image Multilingual Machine Translation (2025.emnlp-main)
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| Challenge: | Current research on in-image machine translation focuses on synthetic data with simple background, single font, fixed text position, and bilingual translation. |
| Approach: | They propose an end-to-end model to handle the challenge of practical conditions in PRIM . they annotate a real-world one-line text image with complex background, fonts, diverse text positions . |
| Outcome: | The proposed model improves translation quality and visual effect compared to other models. |
DocMEdit: Towards Document-Level Model Editing (2025.findings-acl)
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| Challenge: | Existing models only output short phrases or sentences, raising doubts about their practical usability. |
| Approach: | They propose a dataset focused on document-level model editing that aims to correct errors and outdated knowledge in Large language models (LLMs) they propose to use document-based model editing to improve model capabilities in real-world scenarios. |
| Outcome: | The proposed model editing task improves model capabilities in real-world scenarios and reduces the cost of retraining. |
HomeBench: Evaluating LLMs in Smart Homes with Valid and Invalid Instructions Across Single and Multiple Devices (2025.acl-long)
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| Challenge: | Existing state-of-the-art LLMs cannot perform well in situations where instructions are invalid or multiple devices are involved. |
| Approach: | They propose to integrate large language models into smart home assistants by enhancing their ability to accurately understand user needs and respond appropriately. |
| Outcome: | The proposed dataset is the first with valid and invalid instructions across devices . it achieves only 0.0% success rate in the scenario of invalid multi-device instructions . |
PEC-Home: Interpretation of Progressively Elliptical Commands in Smart Homes (2026.findings-acl)
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| Challenge: | Existing home assistants struggle to interpret elliptical commands based on ellipine expressions . current assistants overlook the progressive omission that occurs in human dialogue as context accumulates - limiting their effectiveness in real-world applications . |
| Approach: | They propose a simulated home dataset specifically designed for interpreting progressively elliptical commands in smart homes. |
| Outcome: | The proposed dataset shows that existing home assistants struggle to execute user-intended operations based solely on elliptical commands. |
ChatMusician: Understanding and Generating Music Intrinsically with LLM (2024.findings-acl)
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Ruibin Yuan, Hanfeng Lin, Yi Wang, Zeyue Tian, Shangda Wu, Tianhao Shen, Ge Zhang, Yuhang Wu, Cong Liu, Ziya Zhou, Liumeng Xue, Ziyang Ma, Qin Liu, Tianyu Zheng, Yizhi Li, Yinghao Ma, Yiming Liang, Xiaowei Chi, Ruibo Liu, Zili Wang, Chenghua Lin, Qifeng Liu, Tao Jiang, Wenhao Huang, Wenhu Chen, Jie Fu, Emmanouil Benetos, Gus Xia, Roger Dannenberg, Wei Xue, Shiyin Kang, Yike Guo
| Challenge: | Despite LLMs' impressive capabilities in musical knowledge, music reasoning remains an unsolved task. |
| Approach: | They propose an open-source large language model (LLM) that integrates intrinsic musical abilities into LLaMA2 and GPT-3.5. |
| Outcome: | The proposed model can understand and generate music with a pure text tokenizer without external multi-modal neural structures or tokenizers. |
In-Image Neural Machine Translation with Segmented Pixel Sequence-to-Sequence Model (2023.findings-emnlp)
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| Challenge: | In-Image Machine Translation (IIMT) aims to convert images containing texts from one language to another. |
| Approach: | They propose an end-to-end model instead of the traditional cascade methods which use optical character recognition followed by neural machine translation and text rendering. |
| Outcome: | The proposed model outperforms both cascade methods and current model in translation quality and robustness across various dimensions. |
Beyond Literal Mapping: Benchmarking and Improving Non-Literal Translation Evaluation (2026.acl-long)
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| Challenge: | Large Language Models (LLMs) have advanced machine translation (MT) a meta-evaluation dataset focused on non-literal translations is lacking . experimental results show the inaccuracies of traditional MT metrics and the limitations of LLM-as-a-Judge. |
| Approach: | They propose a meta-evaluation framework that leverages sub-agents to evaluate machine translation metrics. |
| Outcome: | The proposed framework improves on the knowledge cutoff and score inconsistency problem. |
Mem2Evolve: Towards Self-Evolving Agents via Co-Evolutionary Capability Expansion and Experience Distillation (2026.acl-long)
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Zihao Cheng, Zeming Liu, Yingyu Shan, Xinyi Wang, Xiangrong Zhu, Yunpu Ma, Hongru Wang, Yuhang Guo, Wei Lin, Yunhong Wang
| Challenge: | Existing frameworks that focus on static tools and static assets are ineffective for self-evolving agents. |
| Approach: | They propose a paradigm of co-evolutionary Capability Expansion and Experience Distillation that leverages accumulated experience to guide dynamic creation of assets. |
| Outcome: | The proposed framework improves performance in single-task and cross-task settings by 18.53% over standard LLMs, 11.80% over agents evolving solely through experience, and 6.46% over those evolving solelly through asset creation. |
PEAP: Proactive Embodied Action Sequence Planning with Joint Understanding of Vision and Audio Perception (2026.acl-long)
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| Challenge: | Embodied action sequence planning focuses on the capability of embodied agents to implement action planning via environmental perception without explicit human instructions. |
| Approach: | They propose to use a multimodal dataset to evaluate the performance of multiple large language models to evaluate their models' environmental perception capabilities. |
| Outcome: | The proposed model shows that it lacks accurate environmental perception capabilities and that it can improve on the PEAP dataset. |
FinMRAGBench: A Realistic and Complex Benchmark for Multi-Modal RAG in Financial Document Analysis (2026.findings-acl)
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Shouqing Yang, Qi Zhang, Yuhang Yang, Ruikang Xu, Yuwei Hou, Zhulin Jia, Lirong Gao, Haobo Wang, Jinglei Chen, Jiexiang Wang, Sheng Guo, Bo Zheng, Gang Chen
| Challenge: | Existing benchmarks for realistic financial analysis fail to capture realistic financial situations involving cross-document retrieval, multi-page evidence integration, and diverse analytical tasks. |
| Approach: | They propose a multi-modal financial RAG benchmark that evaluates large language models in realistic financial analysis settings. |
| Outcome: | The proposed framework achieves the strongest overall performance across all models. |
Medical Dialogue System: A Survey of Categories, Methods, Evaluation and Challenges (2024.findings-acl)
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Xiaoming Shi, Zeming Liu, Li Du, Yuxuan Wang, Hongru Wang, Yuhang Guo, Tong Ruan, Jie Xu, Xiaofan Zhang, Shaoting Zhang
| Challenge: | Existing medical dialogue systems have significant potential to simplify diagnostic procedure and reduce the cost of collecting information from patients. |
| Approach: | They analyze 325 papers from well-known computer science, natural language processing conferences and journals to find out the major challenges of medical dialog systems. |
| Outcome: | The proposed systems have been surveyed in the medical community but have not been evaluated from a technical perspective. |
SafeToolBench: Pioneering a Prospective Benchmark to Evaluating Tool Utilization Safety in LLMs (2025.findings-emnlp)
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| Challenge: | Existing approaches fail to fully capture all risks in tool utilization, resulting in financial loss or privacy leaking. |
| Approach: | They propose a framework to assess the safety of LLM tool utilization in a prospective manner, covering malicious user instructions and diverse practical toolsets. |
| Outcome: | The proposed framework significantly enhances LLMs’ self-awareness, enabling a more safer and trustworthy tool utilization. |
Deterministic Reversible Data Augmentation for Neural Machine Translation (2024.findings-acl)
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| Challenge: | Recent neural machine translation models have improved translation quality but they also introduce small perturbations like misspelling and paraphrasing. |
| Approach: | They propose a method that generates multi-granularity subword representations with reversible operations and deterministic segmentations. |
| Outcome: | The proposed method outperforms strong baselines on several translation tasks with a clear margin and exhibits good robustness in noisy, low-resource, and cross-domain datasets. |