Papers by Yuhang Liu

HAF-RM: A Hybrid Alignment Framework for Reward Model Training (2025.acl-long)

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.

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Shujun Liu, Xiaoyu Shen, Yuhang Lai, Siyuan Wang, Shengbin Yue, Zengfeng Huang, Xuanjing Huang, Zhongyu Wei

From Outcome to Process: Optimizing MoE Load Balancing with MCTS (2026.findings-acl)

Challenge:	Recent studies have focused on enhancing reward models through data improvements, following the conventional training framework for reward models that directly optimizes the predicted rewards.
Approach:	They propose a hybrid alignment framework HAF-RM that incorporates additional constraint on token-level policy probabilities in addition to the reward score.
Outcome:	The proposed framework can supervise the internal preference model at the token level and optimize the mapping layer of the reward model at sequence level.

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Wenjun Ke, Hengyuan Xu, Ziyu Shang, Yao He, Jiahao Wang, Zijie Xu, Peng Wang, Yuhang Lou, Jiajun Liu

Green & Sustainable NLP

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%.

Where Fact Ends and Fairness Begins: Redefining AI Bias Evaluation through Cognitive Biases (2025.findings-emnlp)

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Jen-tse Huang, Yuhang Yan, Linqi Liu, Yixin Wan, Wenxuan Wang, Kai-Wei Chang, Michael R. Lyu

Outlier Suppression+: Accurate quantization of large language models by equivalent and effective shifting and scaling (2023.emnlp-main)

Challenge:	Existing benchmarks conflate factual correctness and normative fairness . a model may generate responses that are factually accurate but socially unfair .
Approach:	They propose a benchmark to examine the boundary between fact and fair . they draw on representativeness bias, attribution bias and ingroup–outgroup bias to explain why models often misalign fact and faireness.
Outcome:	The proposed model is based on ten frontier models and is available on github . it is compared with a standard model that generates people of color in Nazi-era uniforms .

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Xiuying Wei, Yunchen Zhang, Yuhang Li, Xiangguo Zhang, Ruihao Gong, Jinyang Guo, Xianglong Liu

RepoDebug: Repository-Level Multi-Task and Multi-Language Debugging Evaluation of Large Language Models (2025.findings-emnlp)

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.

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Jingjing Liu, Zeming Liu, Zihao Cheng, Mengliang He, Xiaoming Shi, Yuhang Guo, Xiangrong Zhu, Yuanfang Guo, Yunhong Wang, Haifeng Wang

ALaRM: Align Language Models via Hierarchical Rewards Modeling (2024.findings-acl)

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.

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Yuhang Lai, Siyuan Wang, Shujun Liu, Xuanjing Huang, Zhongyu Wei

FAME: Towards Factual Multi-Task Model Editing (2024.emnlp-main)

Challenge:	Current alignment approaches struggle with inconsistency and sparsity of human supervision signals.
Approach:	They propose a framework modeling hierarchical rewards in reinforcement learning from human feedback (RLHF) it integrates holistic rewards with aspect-specific rewards to enhance alignment of large language models with human preferences.
Outcome:	The proposed framework improves the alignment of large language models with human preferences by integrating holistic rewards with aspect-specific rewards.

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Li Zeng, Yingyu Shan, Zeming Liu, Jiashu Yao, Yuhang Guo

PRISM: Probabilistic Reward Model with Inherent Structural Modeling (2026.acl-long)

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.

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Yuhang Zhou, Yixin Cao, Yuchen Ni, Shihan Dou, Xutian Chen, Ge Zhang, Xiang Liu, Guangnan Ye

Cognitive Modeling

Challenge:	Existing evaluators compress diverse human judgments into a single scalar, leading to brittle alignment and reward hacking.
Approach:	They propose a Gaussian-based reinterpretation of reward evaluation as a conditional distribution and a mixture of Gaussians to capture conflicting preference dimensions.
Outcome:	The proposed model outperforms scalar baselines in accuracy and generalization.

TED-EL: A Corpus for Speech Entity Linking (2024.lrec-main)

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Silin Li, Ruoyu Song, Tianwei Lan, Zeming Liu, Yuhang Guo

Scalable Prompt Generation for Semi-supervised Learning with Language Models (2023.findings-eacl)

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%.

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Yuhang Zhou, Suraj Maharjan, Beiye Liu

Low-Resource NLP

Challenge:	Prompt-based learning methods in semi-supervised learning (SSL) settings have been shown to be effective on multiple natural language understanding datasets and tasks.
Approach:	They propose to use a set of prompt tokens to create diverse prompt models and a varying number of soft prompt token to encourage language models to learn different prompts.
Outcome:	The proposed method achieves the best average accuracy of 71.5% in different few-shot learning settings.

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

Green & Sustainable NLP

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.

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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Zihao Cheng, Hongru Wang, Zeming Liu, Yuhang Guo, Yuanfang Guo, Yunhong Wang, Haifeng Wang

Explore Spurious Correlations at the Concept Level in Language Models for Text Classification (2024.acl-long)

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.

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Yuhang Zhou, Paiheng Xu, Xiaoyu Liu, Bang An, Wei Ai, Furong Huang

Mixture-of-Minds: Multi-Agent Reinforcement Learning for Table Understanding (2026.acl-long)

Challenge:	Language models have demonstrated remarkable performance in numerous NLP tasks, employing both fine-tuning and in-context learning (ICL) methods.
Approach:	They propose a method to assess concept bias in models during fine-tuning and in-context learning using ChatGPT.
Outcome:	The proposed method outperforms token removal approaches and is validated through extensive testing.

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Yuhang Zhou, Mingrui Zhang, Ke Li, Mingyi Wang, Qiao Liu, Qifei Wang, Jiayi Liu, Fei Liu, Serena Li, Weiwei LI, Mingze Gao, Abhishek Kumar, Xiangjun Fan, Zhuokai Zhao, Lizhu Zhang

Automatic Evaluate Dialogue Appropriateness by Using Dialogue Act (2023.findings-emnlp)

Challenge:	Large language models (LLMs) have shown promise on understanding and reasoning over tables, but current approaches remain limited.
Approach:	They propose a multi-agent framework that decomposes table reasoning into three specialized roles: planning, coding, and answering.
Outcome:	The proposed framework decomposes table reasoning into three specialized roles: planning, coding, and answering.

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Bao Chen, Yuanjie Wang, Zeming Liu, Yuhang Guo

Dialogue Systems & Conversational Agents

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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Yanzhi Tian, Zeming Liu, Zhengyang Liu, Yuhang Guo

PRIM: Towards Practical In-Image Multilingual Machine Translation (2025.emnlp-main)

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 .

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Yanzhi Tian, Zeming Liu, Zhengyang Liu, Chong Feng, Xin Li, Heyan Huang, Yuhang Guo

Multilinguality

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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Li Zeng, Zeming Liu, Chong Feng, Heyan Huang, Yuhang Guo

InfiGUIAgent: A Multimodal Generalist GUI Agent with Native Reasoning and Reflection (2026.eacl-long)

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.

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Yuhang Liu, Pengxiang Li, Zishu Wei, Congkai Xie, Xueyu Hu, Xinchen Xu, Shengyu Zhang, Xiaotian Han, Hongxia Yang, Fei Wu

HomeBench: Evaluating LLMs in Smart Homes with Valid and Invalid Instructions Across Single and Multiple Devices (2025.acl-long)

Challenge:	Existing GUI Agents face challenges in multi-step reasoning and reliance on textual annotations, limiting their effectiveness.
Approach:	They propose an MLLM-based GUI Agent with a two-stage supervised fine-tuning pipeline that enhances GUI understanding and grounding.
Outcome:	InfiGUIAgent achieves competitive performance on several GUI benchmarks, highlighting the impact of native reasoning skills in enhancing GUI interaction for automation tasks.

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Silin Li, Yuhang Guo, Jiashu Yao, Zeming Liu, Haifeng Wang

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 .

Graph vs. Sequence: An Empirical Study on Knowledge Forms for Knowledge-Grounded Dialogue (2023.emnlp-main)

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Yizhe Yang, Heyan Huang, Yuhang Liu, Yang Gao

Natural Language Interfaces

Challenge:	Knowledge-grounded dialogue systems can generate informative responses based on dialogue history and external knowledge source.
Approach:	They conduct a thorough experiment to determine the optimal knowledge form, mutual effects between knowl- edge and model selection, and the few-shot performance of knowledge.
Outcome:	The proposed method combines knowledge-grounded dialogue with human-generated dialogues to generate informative and meaningful responses.

Generative Music Models’ Alignment with Professional and Amateur Users’ Expectations (2025.findings-acl)

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Zihao Wang, Jiaxing Yu, Haoxuan Liu, Zehui Zheng, Yuhang Jin, Shuyu Li, Shulei Ji, Kejun Zhang

ReLook: Vision-Grounded RL with a Multimodal LLM Critic for Agentic Web Coding (2026.acl-long)

Challenge:	Recent years have witnessed rapid advances in text-to-music generation using large language models.
Approach:	They propose a task to align AI-generated music with human expressions . they use a dataset of over 1.5 million songs to analyze their content .
Outcome:	The proposed framework outperforms baseline models and facilitates end-to-end generation of songs audio.

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Yuhang Li, Chenchen Zhang, Ruilin Lv, Ao Liu, Ken Deng, Yuanxing Zhang, Jiaheng Liu, Bo Zhou

DIFFA-2: A Practical Diffusion Large Language Model for General Audio Understanding (2026.findings-acl)

Challenge:	Large Language Models (LLMs) excel at algorithmic code generation, but front-end development is lacking in visual fidelity and interaction.
Approach:	They propose an agentic, vision-grounded reinforcement learning framework that closes a loop by invoking a multimodal LLM as a tool.
Outcome:	The proposed framework outperforms baselines in front-end code generation.

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Jiaming Zhou, Xuxin Cheng, Shiwan Zhao, Yuhang Jia, Cao Liu, Ke Zeng, Xunliang Cai, Yong Qin

Multi-Hop Knowledge Editing via Critic-Guided Multi-Agent Reasoning (2026.findings-acl)

Challenge:	Autoregressive (AR) large audio language models are expensive in data and computation . prior work shows diffusion-based LALMs can improve audio understanding under matched settings .
Approach:	They propose a diffusion-based LALM that upgrades the speech encoder and employs dual semantic and acoustic adapters.
Outcome:	a new model improves over existing autoregressive large language models and is competitive to strong AR models . the proposed model can make use of limited training data and improve inference efficiency . a recent study shows that diffusion-based models can improve audio understanding .

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Xudong Li, Yuhang Tian, Dandan Song, Zhijing Wu, Shuhao Zhang, Jun Yang, Yongyu Huo, Changzhi Zhou, Xinyu Zhang, Chenhao Li, Huipeng Ma, Luan Zhang, Yan Xu, Qian Liu

Reasoning

Challenge:	Existing knowledge editing methods rely on unidirectional, feed-forward pipelines . a minor retrieval error or logical mismatch at an early hop can become a silent failure .
Approach:	They propose a framework for closed-loop post-edit reasoning that uses a Critic agent to verify coherence and step-wise correctness.
Outcome:	Experiments on MQuAKE-2002 and MQuADE-hard show that CARE effectively mitigates error propagation . a minor retrieval error or logical mismatch at an early hop can become a silent failure .

InternLM-XComposer2.5-Reward: A Simple Yet Effective Multi-Modal Reward Model (2025.findings-acl)

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Yuhang Zang, Xiaoyi Dong, Pan Zhang, Yuhang Cao, Ziyu Liu, Shengyuan Ding, Shenxi Wu, Yubo Ma, Haodong Duan, Wenwei Zhang, Kai Chen, Dahua Lin, Jiaqi Wang

Large Language Models and Causal Inference in Collaboration: A Comprehensive Survey (2025.findings-naacl)

Challenge:	Despite the promising performance of Large Vision Language Models, they sometimes generate incorrect outputs.
Approach:	They propose a multi-modal reward model that aligns LVLMs with human preferences.
Outcome:	The proposed model achieves excellent results on the latest multi-modal reward model benchmark and shows competitive performance on text-only reward model.

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Xiaoyu Liu, Paiheng Xu, Junda Wu, Jiaxin Yuan, Yifan Yang, Yuhang Zhou, Fuxiao Liu, Tianrui Guan, Haoliang Wang, Tong Yu, Julian McAuley, Wei Ai, Furong Huang

PEC-Home: Interpretation of Progressively Elliptical Commands in Smart Homes (2026.findings-acl)

Challenge:	Large Language Models (LLMs) have shown great potential to enhance Natural Language Processing (NLP) models in areas such as predictive accuracy, fairness, robustness, and explainability.
Approach:	They evaluate or improve generative Large Language Models from a causal perspective in areas such as reasoning capacity, fairness and safety issues, explainability, and handling multimodality.
Outcome:	The proposed models can be used to perform causal relationship discovery and causal effect estimation tasks.

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Yingyu Shan, Zeming Liu, Silin Li, Boao Qian, Jiashu Yao, Yuhang Guo, Haifeng Wang

DISCO Balances the Scales: Adaptive Domain- and Difficulty-Aware Reinforcement Learning on Imbalanced Data (2025.findings-emnlp)

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.

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Yuhang Zhou, Jing Zhu, Shengyi Qian, Zhuokai Zhao, Xiyao Wang, Xiaoyu Liu, Ming Li, Paiheng Xu, Wei Ai, Furong Huang

Fundamental Capabilities of Large Language Models and their Applications in Domain Scenarios: A Survey (2024.acl-long)

Challenge:	Large Language Models (LLMs) are increasingly aligned with human preferences through Reinforcement Learning from Human Feedback (RLHF).
Approach:	a new study proposes a domain-informed self-consistency policy optimization extension to GRPO that addresses inter-group imbalance.
Outcome:	a new extension of GRPO addresses inter-group imbalance with two key innovations . the proposed method outperforms existing GR PO variants by 5% on Qwen3 models .

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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

EvoR: Evolving Retrieval for Code Generation (2024.findings-emnlp)

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.

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Hongjin Su, Shuyang Jiang, Yuhang Lai, Haoyuan Wu, Boao Shi, Che Liu, Qian Liu, Tao Yu

Text Generation

Challenge:	Existing pipelines for retrieval-augmented code generation (RACG) use static knowledge bases with a single source, limiting adaptation capabilities of Large Language Models (LLMs) Extensive experiments demonstrate that EVOR achieves two to four times of execution accuracy compared to other methods such as Reflexion.
Approach:	They propose a retrieval-augmented code generation pipeline that employs the synchronous evolution of queries and diverse knowledge bases.
Outcome:	The proposed pipeline achieves two to four times of execution accuracy compared to other methods.

CADMate: Generating CAD Assembly Plan with Geometric Chain-of-Thought and Spatial Physical Rewards (2026.acl-long)

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Jiali Chen, DingBa Fu, Xusen Hei, Yuhang Liu, Yiyang Chen, Jiayuan Xie, Wenqi Fan, Yi Cai

Reasoning

Challenge:	Computer-aided design (CAD) is crucial in prototyping complex 3D objects . designers manually define assembly sequences for individual CAD parts .
Approach:	They propose a framework for computer-aided design that predicts actions for CAD parts . they use a reference design image and disassembled parts to generate 6-DoF transformations .
Outcome:	The proposed framework outperforms existing MLLMs in the design of CAD assemblies.

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

Prove Your Point!: Bringing Proof-Enhancement Principles to Argumentative Essay Generation (2024.emnlp-main)

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.

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Ruiyu Xiao, Lei Wu, Yuhang Gou, Weinan Zhang, Ting Liu

Challenge:	Existing arguments generation methods often overlook connections between opinions . Existing methods struggle with providing compelling proof .
Approach:	They propose a two-stage framework for argumentative essay generation with a focus on logical enhancement.
Outcome:	The proposed framework generates argumentative essays with better logical validity and persuasiveness than baseline models.

In-Image Neural Machine Translation with Segmented Pixel Sequence-to-Sequence Model (2023.findings-emnlp)

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Yanzhi Tian, Xiang Li, Zeming Liu, Yuhang Guo, Bin Wang

Multilinguality

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.

DLPO: Towards a Robust, Efficient, and Generalizable Prompt Optimization Framework from a Deep-Learning Perspective (2025.findings-emnlp)

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Dengyun Peng, Yuhang Zhou, Qiguang Chen, JinHao Liu, Jingjing Chen, Libo Qin, Wanxiang Che

Beyond Literal Mapping: Benchmarking and Improving Non-Literal Translation Evaluation (2026.acl-long)

Challenge:	Existing methods for prompt optimization still face challenges in robustness, efficiency, and generalization.
Approach:	They propose 7 new approaches inspired by traditional deep learning paradigms for prompt optimization that integrate text-based gradient optimization.
Outcome:	The proposed methods integrate deep learning paradigms into text-based gradient optimization.

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Yanzhi Tian, Cunxiang Wang, Zeming Liu, Heyan Huang, Wenbo Yu, Dawei Song, Jie Tang, Yuhang Guo

Machine Translation

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.

DINT Transformer (2025.emnlp-main)

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Yueyang Cang, Yuhang Liu, Xiaoteng Zhang, Erlu Zhao, Li Shi

Mem2Evolve: Towards Self-Evolving Agents via Co-Evolutionary Capability Expansion and Experience Distillation (2026.acl-long)

Challenge:	Experimental results show that the DINT Transformer improves accuracy and robustness across practical applications.
Approach:	They propose a differential attention mechanism that suppresses the impact of irrelevant contexts by computing DIF-Ference between two independent attention distributions.
Outcome:	The proposed architecture improves numerical stability and ability to capture global dependencies.

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Zihao Cheng, Zeming Liu, Yingyu Shan, Xinyi Wang, Xiangrong Zhu, Yunpu Ma, Hongru Wang, Yuhang Guo, Wei Lin, Yunhong Wang

Mementos: A Comprehensive Benchmark for Multimodal Large Language Model Reasoning over Image Sequences (2024.acl-long)

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.

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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

PEAP: Proactive Embodied Action Sequence Planning with Joint Understanding of Vision and Audio Perception (2026.acl-long)

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.

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Tianwei Lan, Jiaqi Wu, Zeming Liu, Zhaoxin Fan, Haifeng Wang, Yuhang Guo

ReEx-SQL: Reasoning with Execution-Aware Reinforcement Learning for Text-to-SQL (2026.acl-long)

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.

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Yaxun Dai, Wenxuan Xie, Xialie Zhuang, Tianyu Yang, Ziyi Liu, Haiqin Yang, Yiying Yang, Yuhang Zhao, Pingfu Chao, Wenhao Jiang

Programming Languages in NLP

Challenge:	Current Text-to-SQL reasoning models lack integrated execution feedback during generation.
Approach:	They propose a text-to-SQL framework that interacts with the SQL execution engine during decoding and dynamically adjusts reasoning based on execution feedback.
Outcome:	The proposed framework achieves 89.1% accuracy on Spider and 65.3% on BIRD at the 7B scale.

TacoPrompt: A Collaborative Multi-Task Prompt Learning Method for Self-Supervised Taxonomy Completion (2023.emnlp-main)

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Hongyuan Xu, Ciyi Liu, Yuhang Niu, Yunong Chen, Xiangrui Cai, Yanlong Wen, Xiaojie Yuan

Low-Resource NLP

Challenge:	Existing methods for automating taxonomy completion use subtasks to learn subtask results, ignoring the effects of subtask on the final prediction.
Approach:	They propose a multi-task automatic taxonomy completion method that attaches emerging concepts to an appropriate pair of hypernym and hyponym in existing taxonomies.
Outcome:	The proposed method improves on three datasets and improves inference efficiency.

TIGER: Text-Informed Generalized Enzyme-Reaction Retrieval (2026.acl-long)

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Yuhang Zhang, Keyan Ding, Peilin Chen, Han Liu, Can Lin, Ruixi Chen, Shiqi Wang, Qi Song

Information Retrieval

Challenge:	Existing approaches to enzyme–reaction retrieval suffer from poor generalization across tasks and distributions . TIGER is a text-informed generalized enzyme-reaction retrieval framework that bridges enzymes and biochemical reactions.
Approach:	They propose a text-informed generalized enzyme-reaction retrieval framework that leverages protein-to-text generation models to distill textual knowledge from enzyme sequences.
Outcome:	The proposed framework outperforms state-of-the-art methods in enzyme–reaction retrieval tasks and distributions.

Subgraph-Guided Executable Logical Form Generation for Knowledge Base Question Answering (2026.findings-acl)

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Yuhang Tian, Dandan Song, Zhijing Wu, Changzhi Zhou, Jun Yang, Huipeng Ma, Chenhao Li, Luan Zhang, Yading Li, Xudong Li, Shenxi Liu, Jing Jiang

Natural Language Interfaces

Challenge:	Existing retrieval-augmented approaches focus on ignoring the structural information of the Knowledge Base (KB) and the question.
Approach:	They propose a structure-aware subgraph retrieval stage that ranks candidate subgraphs by aligning them with the question’s structure, along with semantic relevance.
Outcome:	Experiments on GrailQA, WebQSP, and GraphQuestions show that the proposed framework achieves state-of-the-art performance.

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

Dialogue Systems & Conversational Agents

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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Hongfei Xia, Hongru Wang, Zeming Liu, Qian Yu, Yuhang Guo, Haifeng Wang

Chain-of-Quizzes: Pedagogy-inspired Example Selection in In-Context-Learning (2024.findings-acl)

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.

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Yiquan Wu, Anlai Zhou, Yuhang Liu, Yifei Liu, Adam Jatowt, Weiming Lu, Jun Xiao, Kun Kuang

Low-Resource NLP

Challenge:	In-context learning (ICL) is a powerful tool for enhancing large language models (LLMs) by mimicking the human learning process.
Approach:	They propose a Chain-of-Quizzes framework that uses LLMs to answer a quiz to sift 'good' examples, combine them iteratively with the increasing complexity, and utilize a final exam to gauge the combined example chains.
Outcome:	The proposed framework outperforms baseline models on diverse reasoning datasets and shows that it is scalable and can be used in future research.

Bridging the Sensory Gap: Visual Injection for Taxonomy Completion (2026.acl-long)

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Yuhang Niu, Hongyuan Xu, Ciyi Liu, Bofan Wei, Jiaqi Ye, Yanlong Wen, Xiaojie Yuan

How Jailbreak Defenses Work and Ensemble? A Mechanistic Investigation (2025.findings-emnlp)

Challenge:	Existing text-only methods suffer from a "Sensory Gap" in integrating new concepts into existing hierarchies.
Approach:	They propose a framework leveraging Visual Injection for Taxonomy Completion that maps synthesized images into intrinsic pseudo-tokens and decouples magnitude from selection to prevent visual signals from being drowned out.
Outcome:	Experiments on three datasets show that VITC achieves state-of-the-art performance . it delivers an average absolute gain of over 19% in Hit@1.

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Zhuohan Long, Siyuan Wang, Shujun Liu, Yuhang Lai

Unleashing Spatial Reasoning in Multimodal Large Language Models via Textual Representation Guided Reasoning (2026.acl-long)

Challenge:	Jailbreak attacks, where harmful prompts bypass generative models’ built-in safety, raise serious concerns about model vulnerability.
Approach:	They propose to reframe the standard generation task as a binary classification problem to assess model refusal tendencies for both harmful and benign queries.
Outcome:	The proposed defenses improve model safety or optimize the trade-off between safety and helpfulness.

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Jiacheng Hua, Yishu Yin, Yuhang Wu, Tai Wang, Yifei Huang, Miao Liu

RiTTA: Modeling Event Relations in Text-to-Audio Generation (2025.emnlp-main)

Challenge:	Existing Multimodal Large Language Models struggle with 3D spatial reasoning as they fail to construct structured abstractions of the 3D environment depicted in video inputs.
Approach:	They propose a prompting method that induces MLLMs to generate 3D representations as reasoning traces for more accurate spatial question answering.
Outcome:	Extensive experiments on VSI-Bench and OST-Bech show that TRACE improves over prior prompting strategies across a diverse range of MLLM backbones.

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Yuhang He, Yash Jain, Xubo Liu, Andrew Markham, Vibhav Vineet

Speech & Audio in NLP

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.

SlideCoder: Layout-aware RAG-enhanced Hierarchical Slide Generation from Design (2025.emnlp-main)

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Wenxin Tang, Jingyu Xiao, Wenxuan Jiang, Xi Xiao, Yuhang Wang, Xuxin Tang, Qing Li, Yuehe Ma, Junliang Liu, Shisong Tang, Michael R. Lyu

Multi-Stage Balanced Distillation: Addressing Long-Tail Challenges in Sequence-Level Knowledge Distillation (2024.findings-emnlp)

Challenge:	Existing natural language-based LLM generation methods struggle to capture visual and structural nuances of slide designs.
Approach:	They propose a layout-aware framework for generating editable slides from reference images . they propose python code that translates NL instructions into Python code to construct each slide .
Outcome:	The proposed framework outperforms state-of-the-art models by up to 40.5 points . it also outperformed open-source models with improved reverse-engineered data.

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Yuhang Zhou, Jing Zhu, Paiheng Xu, Xiaoyu Liu, Xiyao Wang, Danai Koutra, Wei Ai, Furong Huang

Deterministic Reversible Data Augmentation for Neural Machine Translation (2024.findings-acl)

Challenge:	Knowledge distillation (KD) is a promising solution for large language models, but their deployment remains computationally expensive.
Approach:	They propose a framework which iteratively balances training data within a fixed computational budget and enables the transfer of knowledge from expensive teacher LLMs to smaller student models.
Outcome:	The proposed framework achieves state-of-the-art performance across diverse long-tailed datasets, enhancing both the efficiency and efficacy of the distilled models.

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Jiashu Yao, Heyan Huang, Zeming Liu, Yuhang Guo

Machine Translation

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.

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

R3-NL2GQL: A Model Coordination and Knowledge Graph Alignment Approach for NL2GQL (2024.findings-emnlp)

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.

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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