Papers by Haoyu Zhang

ToolGrad: Efficient Tool-use Dataset Generation with Textual “Gradients” (2026.findings-acl)

Challenge:	Spreadsheets are characterized by their extensive two-dimensional grids, flexible layouts, and varied formatting options, which pose significant challenges for large language models (LLMs).
Approach:	They propose a structural-anchor-based compression, inverse index translation, and data-format-aware aggregation module to compress spreadsheets effectively.
Outcome:	The proposed method outperforms the existing model in GPT4 and achieves a state-of-the-art 78.9% F1 score.

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Zhongyi Zhou, Kohei Uehara, Haoyu Zhang, Jingtao Zhou, Lin Gu, Ruofei Du, Zheng Xu, Tatsuya Harada

TC–RAG: Turing–Complete RAG’s Case study on Medical LLM Systems (2025.acl-long)

Challenge:	Prior work synthesizes tool-use LLM datasets by first generating a user query, then complex tool-using annotations like DFS.
Approach:	They propose an agentic framework that synthesizes user queries and generates valid tool-use chains . they propose a dataset with more complex tool use, lower cost, and almost 100% pass rate .
Outcome:	Experiments show that tools trained on ToolGrad outperform expensive baseline datasets and proprietary LLMs.

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Xinke Jiang, Yue Fang, Rihong Qiu, Haoyu Zhang, Yongxin Xu, Hao Chen, Wentao Zhang, Ruizhe Zhang, Yuchen Fang, Xinyu Ma, Xu Chu, Junfeng Zhao, Yasha Wang

Information Retrieval

Challenge:	Existing approaches to RAG neglect system state variables, resulting in poor performance and erroneous knowledge accumulation.
Approach:	They propose a framework that incorporates a Turing Complete System to manage state variables and manage retrieval halting.
Outcome:	The proposed framework improves on seven real-world healthcare datasets and shows that it is more accurate than existing methods.

EmoMM: Benchmarking and Steering MLLM for Multimodal Emotion Recognition under Conflict and Missingness (2026.findings-acl)

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Yueru Sun, Yimeng Zhang, Haoyu Gu, Nuo Chen, Dong She, Xianrong Yao, Yang Gao, Zhanpeng Jin

HermEs: Interactive Spreadsheet Formula Prediction via Hierarchical Formulet Expansion (2023.acl-long)

Challenge:	Multimodal Large Language Models (MLLMs) have shown promise in MER, but their internal decision-making mechanisms under modality conflict and missingness remain underexplored.
Approach:	They propose a multimodal large language model that can detect and control modality conflicts and missing subsets by a lightweight mechanism that detects and controls modality conflict.
Outcome:	The proposed framework improves performance across settings, showing it can handle conflict and missing behaviors.

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Wanrong He, Haoyu Dong, Yihuai Gao, Zhichao Fan, Xingzhuo Guo, Zhitao Hou, Xiao Lv, Ran Jia, Shi Han, Dongmei Zhang

Challenge:	HermEs is a spreadsheet formula prediction language that is difficult for Excel users without programming experience to master.
Approach:	They propose a hierarchical approach to formula prediction via HiEraRchical forMulet ExpanSion . they propose generating formulas in a fixed order using hierarchically generated formulas .
Outcome:	The proposed approach improves formula prediction accuracy by guaranteeing correct grammar and streamlining token-level decoding with high-level Formulet.

End-to-End Modeling via Information Tree for One-Shot Natural Language Spatial Video Grounding (2022.acl-long)

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Mengze Li, Tianbao Wang, Haoyu Zhang, Shengyu Zhang, Zhou Zhao, Jiaxu Miao, Wenqiao Zhang, Wenming Tan, Jin Wang, Peng Wang, Shiliang Pu, Fei Wu

Event Semantic Classification in Context (2024.findings-eacl)

Challenge:	Existing methods for grounding video frames with dense annotations require enormous amount of human effort.
Approach:	They propose to ground natural language in video frames with only one frame labeled . they propose an end-to-end model that eliminates interference of irrelevant frames .
Outcome:	The proposed model can ground natural language in all video frames with only one frame labeled . the proposed model eliminates interference of irrelevant frames based on branch search and cropping techniques .

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Haoyu Wang, Hongming Zhang, Kaiqiang Song, Dong Yu, Dan Roth

Unleashing the Power of Language Models in Text-Attributed Graph (2023.findings-emnlp)

Challenge:	In this work, we focus on the semantic classification of events in context to help machines gain a deeper understanding of events.
Approach:	They propose to integrate event semantics into downstream tasks to help machines understand events better.
Outcome:	The proposed model improves the understanding of events in context.

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Haoyu Kuang, Jiarong Xu, Haozhe Zhang, Zuyu Zhao, Qi Zhang, Xuanjing Huang, Zhongyu Wei

MAIN: Mutual Alignment Is Necessary for instruction tuning (2025.emnlp-main)

Challenge:	Existing studies on graph learning on text-attributed graphs have been limited by memory cost and underutilization of relationships between nodes and words.
Approach:	They propose a Node Representation Update Pre-training Architecture based on Co-modeling text and graph to learn representations of papers and words simultaneously.
Outcome:	The proposed model outperforms baselines on the ogbn-arxiv benchmark dataset.

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Fanyi Yang, Jianfeng Liu, Xin Zhang, Haoyu Liu, Xixin Cao, Yuefeng Zhan, Hao Sun, Weiwei Deng, Feng Sun, Qi Zhang

Are All Steps Equally Important? Benchmarking Essentiality Detection in Event Processes (2023.emnlp-main)

Challenge:	Instruction tuning has enabled large language models to achieve remarkable performance, yet its success heavily depends on the availability of high-quality instruction-response pairs.
Approach:	They propose a mutual alignment framework which enforces coherence between instructions and responses through mutual constraints.
Outcome:	The proposed framework generalizes well across model architectures and sizes, achieving state-of-the-art performance on LLaMA, Mistral, and Qwen models across diverse benchmarks.

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Haoyu Wang, Hongming Zhang, Yueguan Wang, Yuqian Deng, Muhao Chen, Dan Roth

Commonsense Reasoning

Challenge:	Existing models of event processing do not understand the essentiality of step events towards a goal event.
Approach:	They propose to deconstruct a goal event into a discrete representation of finer-grained (step) events, which are not equally important to the goal.
Outcome:	The proposed model can understand the essentiality of different step events towards a goal event.

RoseRAG: Robust Retrieval-augmented Generation with Small-scale LLMs via Margin-aware Preference Optimization (2025.findings-acl)

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Tianci Liu, Haoxiang Jiang, Tianze Wang, Ran Xu, Yue Yu, Linjun Zhang, Tuo Zhao, Haoyu Wang

Robustness in NLP

Challenge:	Large language models (LLMs) have impressive performance but face high computational costs and latency, limiting their deployment in resource-constrained settings.
Approach:	They propose a robust RAG framework for large language models via Margin-aware Preference Optimization to enhance the accuracy and reliability of SLMs.
Outcome:	The proposed framework surpasses state-of-the-art benchmarks on three open-domain question answering tasks.

LLM×MapReduce-V3: Enabling Interactive In-Depth Survey Generation through a MCP-Driven Hierarchically Modular Agent System (2025.emnlp-demos)

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Yu Chao, Siyu Lin, Xiaorong Wang, Zhu Zhang, Zihan Zhou, Haoyu Wang, Shuo Wang, Jie Zhou, Zhiyuan Liu, Maosong Sun

Challenge:	Generating high-quality long-form survey articles poses significant challenges to AI Agent systems.
Approach:	They propose a hierarchically modular agent system for long-form survey generation . they use atomic models to implement skeleton initialization, digest construction, and skelet refinement . human evaluations demonstrate system surpasses representative baselines .
Outcome:	The proposed system surpasses representative baselines in both content depth and length, highlighting the strength of MCP-based modular planning.

Analyze Like a Venture Capitalist: Information-Gain and Knowledge Enhanced Graph Reasoning for Startup Success Prediction (2026.findings-acl)

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Haoyu Pei, Zhongyang Liu, Xiangyi Xiao, Xiaocong Du, Suting Hong, Kunpeng Zhang, Haipeng Zhang

CLIP Models are Few-Shot Learners: Empirical Studies on VQA and Visual Entailment (2022.acl-long)

Challenge:	Most venture capital investments fail, while a few deliver outsized returns.
Approach:	They propose a framework that synthesizes relational evidence across sources . they propose combining information-gain-driven retriever and knowledge base to ground reasoning .
Outcome:	The proposed framework achieves +5.9% F1 and +22.1% Precision@5 over state-of-the-art baselines.

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Haoyu Song, Li Dong, Weinan Zhang, Ting Liu, Furu Wei

Visual Data in NLP

Challenge:	Previously, CLIP was only regarded as a powerful visual encoder.
Approach:	They propose a parameter-efficient fine-tuning strategy to boost CLIP's few-shot performance on a visual entailment task without introducing any additional pre-training procedure.
Outcome:	The proposed strategy achieves competitive zero/few-shot results on visual question answering and visual entailment tasks without introducing any additional pre-training procedure.

Learning Language-guided Adaptive Hyper-modality Representation for Multimodal Sentiment Analysis (2023.emnlp-main)

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Haoyu Zhang, Yu Wang, Guanghao Yin, Kejun Liu, Yuanyuan Liu, Tianshu Yu

AgentSense: Benchmarking Social Intelligence of Language Agents through Interactive Scenarios (2025.naacl-long)

Challenge:	Multimodal Sentiment Analysis (MSA) is effective when using rich information from multiple sources, but the potential sentiment-irrelevant information across modalities may hinder the performance from being further improved.
Approach:	They propose an Adaptive Language-guided Multimodal Transformer (ALMT) that learns an irrelevance/conflict-suppressing representation from visual and audio features under guidance of language features at different scales.
Outcome:	The proposed model achieves state-of-the-art on several popular datasets and an abundance of ablation shows the effectiveness of the proposed model.

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Xinyi Mou, Jingcong Liang, Jiayu Lin, Xinnong Zhang, Xiawei Liu, Shiyue Yang, Rong Ye, Lei Chen, Haoyu Kuang, Xuanjing Huang, Zhongyu Wei

Linguistics & Cognitive NLP

Challenge:	Large language models are increasingly employed to empower autonomous agents to simulate human behavior.
Approach:	They propose to evaluate LLM-driven agents through multi-turn interactions using a bottom-up approach to create diverse social scenarios constructed from extensive scripts.
Outcome:	The proposed model evaluates LLM-driven agents through multi-turn interactions emphasizing goal completion and implicit reasoning.

Se2: Sequential Example Selection for In-Context Learning (2024.findings-acl)

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Haoyu Liu, Jianfeng Liu, Shaohan Huang, Yuefeng Zhan, Hao Sun, Weiwei Deng, Furu Wei, Qi Zhang

HammerBench: Fine-Grained Function-Calling Evaluation in Real Mobile Assistant Scenarios (2025.findings-acl)

Challenge:	Prior work has explored the selection of examples for in-context learning, neglecting the internal relationships between examples and exist an inconsistency between training and inference.
Approach:	They propose a sequential-aware method that leverages the LLM’s feedback on varying context, aiding in capturing inter-relationships and sequential information among examples.
Outcome:	Experiments on 23 NLP tasks show that Se2 surpasses baselines and achieves 42% relative improvement over random selection.

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Jun Wang, Jiamu Zhou, Xihuai Wang, Xiaoyun Mo, Haoyu Zhang, Qiqiang Lin, Jincheng Jincheng, Muning Wen, Weinan Zhang, Qiuying Peng, Jun Wang

Natural Language Interfaces

Challenge:	Evaluating the performance of LLMs in multi-turn interactions presents significant challenges due to the complexity and variability of user behavior.
Approach:	They propose a benchmark framework for assessing LLMs’ function-calling capabilities in multi-turn dialogues.
Outcome:	The proposed framework is based on a dataset derived from popular mobile apps and anonymized user logs.

GeAR: Generation Augmented Retrieval (2025.findings-acl)

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Haoyu Liu, Shaohan Huang, Jianfeng Liu, Yuefeng Zhan, Hao Sun, Weiwei Deng, Feng Sun, Furu Wei, Qi Zhang

Information Retrieval

Challenge:	Document retrieval techniques are used to compute semantic similarity between a query and documents, but the scalar similarity fails to reflect enough information, hindering the interpretation of retrieval results.
Approach:	They propose a method which improves the global document-query similarity through contrastive learning and integrates well-designed fusion and decoding modules.
Outcome:	The proposed method improves the global document-query similarity through contrastive learning and integrates well-designed fusion and decoding modules.

Table-LLM-Specialist: Language Model Specialists for Tables using Iterative Fine-tuning (2025.emnlp-main)

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Junjie Xing, Yeye He, Mengyu Zhou, Haoyu Dong, Shi Han, Dongmei Zhang, Surajit Chaudhuri

PLOG: Table-to-Logic Pretraining for Logical Table-to-Text Generation (2022.emnlp-main)

Challenge:	Language models such as GPT and Llama have shown remarkable ability on diverse natural language tasks, yet their performance on complex table tasks is suboptimal.
Approach:	They propose a generator-validator paradigm to iteratively generate-then-validate training data from language models to fine-tune stronger Table-Specialist models that can specialize in a given task, without using manually-labeled data.
Outcome:	The proposed model outperforms vanilla language models on diverse table tasks and can match or surpass GPT-4 level quality.

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Ao Liu, Haoyu Dong, Naoaki Okazaki, Shi Han, Dongmei Zhang

Text Generation

Challenge:	Logical table-to-text generation requires models to derive logical-level facts from table records via logical inference.
Approach:	They propose a pretrained logical form generator framework to improve generation fidelity . they use a dataset to test the logical inference accuracy of the framework .
Outcome:	The proposed framework outperforms baselines on LOGICNLG and CONTLOG on two benchmarks.

RESIN: A Dockerized Schema-Guided Cross-document Cross-lingual Cross-media Information Extraction and Event Tracking System (2021.naacl-demos)

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Haoyang Wen, Ying Lin, Tuan Lai, Xiaoman Pan, Sha Li, Xudong Lin, Ben Zhou, Manling Li, Haoyu Wang, Hongming Zhang, Xiaodong Yu, Alexander Dong, Zhenhailong Wang, Yi Fung, Piyush Mishra, Qing Lyu, Dídac Surís, Brian Chen, Susan Windisch Brown, Martha Palmer, Chris Callison-Burch, Carl Vondrick, Jiawei Han, Dan Roth, Shih-Fu Chang, Heng Ji

Visual Data in NLP

Challenge:	We present a new information extraction system that can construct temporal event graphs from news documents.
Approach:	They propose a temporal event graph extraction system that can extract news documents . they extend the system from sentence-level event extraction to cross-document cross-media event extraction .
Outcome:	The proposed system can extract temporal event graphs from news documents in multiple languages and multiple data modalities.

PILOT: Planning via Internalized Latent Optimization Trajectories for Large Language Models (2026.acl-long)

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Haoyu Zheng, Yun Zhu, Yuqian Yuan, Bo Yuan, Wenqiao Zhang, Siliang Tang, Jun Xiao

RESIN-11: Schema-guided Event Prediction for 11 Newsworthy Scenarios (2022.naacl-demo)

Challenge:	Large Language Models lack the capacity to formulate global strategies due to latency and availability constraints.
Approach:	They propose a framework to internalize the strategic oversight of large models into intrinsic Latent Guidance by synthesizing a query-conditioned Latent Guide.
Outcome:	The proposed framework outperforms strong baselines on mathematical and coding benchmarks with negligible inference latency.

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Xinya Du, Zixuan Zhang, Sha Li, Pengfei Yu, Hongwei Wang, Tuan Lai, Xudong Lin, Ziqi Wang, Iris Liu, Ben Zhou, Haoyang Wen, Manling Li, Darryl Hannan, Jie Lei, Hyounghun Kim, Rotem Dror, Haoyu Wang, Michael Regan, Qi Zeng, Qing Lyu, Charles Yu, Carl Edwards, Xiaomeng Jin, Yizhu Jiao, Ghazaleh Kazeminejad, Zhenhailong Wang, Chris Callison-Burch, Mohit Bansal, Carl Vondrick, Jiawei Han, Dan Roth, Shih-Fu Chang, Martha Palmer, Heng Ji

Not All Tokens Matter: Towards Efficient LLM Reasoning via Token Significance in Reinforcement Learning (2026.acl-long)

Challenge:	Existing methods for event prediction are incomplete and noisy.
Approach:	They propose to use news-related event schemas to extract newsworthy events . they build a demo website and include a video demonstrating the framework .
Outcome:	The proposed framework can be applied to a wide variety of newsworthy scenarios.

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Hanbing Liu, Lang Cao, Yuanyi Ren, Mengyu Zhou, Haoyu Dong, Xiaojun Ma, Shi Han, Dongmei Zhang

Responsible & Trustworthy NLP

Challenge:	Large language models (LLMs) often produce unnecessarily long explanations that reduce efficiency.
Approach:	They propose a length-aware reward that selectively penalizes insignificance tokens . they also propose 'dynamic length control' that encourages more detailed reasoning .
Outcome:	The proposed method reduces response length while maintaining correctness, the authors show . it selectively penalizes insignificance tokens while maintaining accuracy .

LitVISTA: A Benchmark for Narrative Orchestration in Literary Text (2026.acl-long)

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Mingzhe Lu, Yiwen Wang, Yanbing Liu, Qi You, Chong Liu, Ruize Qin, Haoyu Dong, Wenyu Zhang, JiaRui Zhang, Yue Hu, Yunpeng Li

Challenge:	Existing large language models focus on causal coherence, neglecting the complex story arcs and orchestration inherent in human narratives.
Approach:	They propose a high-dimensional framework for narrative orchestration that unifies human and model perspectives while jointly characterizing narrative function and structure in a common space.
Outcome:	The proposed framework unifies human and model perspectives while jointly characterizing narrative function and structure in a common space.

Divide and Denoise: Learning from Noisy Labels in Fine-Grained Entity Typing with Cluster-Wise Loss Correction (2022.acl-long)

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Kunyuan Pang, Haoyu Zhang, Jie Zhou, Ting Wang

Hierarchy-Aware Global Model for Hierarchical Text Classification (2020.acl-main)

Challenge:	Existing FET noise learning methods rely on prediction distributions in instance-independent manner, which causes confirmation bias.
Approach:	They propose a clustering-based loss correction framework to address confirmation bias in FET . they first train a coarse backbone model as a feature extractor and noise estimator .
Outcome:	The proposed framework achieves the best performance over existing systems on three public datasets and is stable to hyperparameters.

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Jie Zhou, Chunping Ma, Dingkun Long, Guangwei Xu, Ning Ding, Haoyu Zhang, Pengjun Xie, Gongshen Liu

Can LLMs be Good Graph Judge for Knowledge Graph Construction? (2025.emnlp-main)

Challenge:	Existing methods for hierarchical text classification are limited and lack holistic structural information.
Approach:	They propose a hierarchy-aware global model with two variants that learn hierarchy-based label embeddings through an encoder and conduct inductive fusion of label-alike text features.
Outcome:	The proposed model improves on three benchmark datasets.

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Haoyu Huang, Chong Chen, Zeang Sheng, Yang Li, Wentao Zhang

Structured Data in NLP

Challenge:	Existing methods for converting unstructured text into structured Knowledge Graphs (KGs) have limitations such as large amount of noise, inaccurate knowledge, and hallucination .
Approach:	They propose a GraphJudge framework to reduce noise in real-world documents . they propose Graphjudge to fine-tune a LLM as a graph judge to enhance quality .
Outcome:	The proposed framework eliminates noise in real-world documents and improves the quality of generated KGs.

BoB: BERT Over BERT for Training Persona-based Dialogue Models from Limited Personalized Data (2021.acl-long)

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Haoyu Song, Yan Wang, Kaiyan Zhang, Wei-Nan Zhang, Ting Liu

Dialogue Systems & Conversational Agents

Challenge:	Existing persona-based dialogue models use crowd-sourced data, such as the PersonaChat . however, the cost of such datasets is limited, and the model is not robust.
Approach:	They propose to disentangle persona-based dialogue generation into two sub-tasks by using a BERT-over-BERT model.
Outcome:	The proposed model outperforms baselines in response quality and persona consistency under different limited data settings.

AI-Press: A Multi-Agent News Generating and Feedback Simulation System Powered by Large Language Models (2025.coling-demos)

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Xiawei Liu, Shiyue Yang, Xinnong Zhang, Haoyu Kuang, Libo Sun, Yihang Yang, Siming Chen, Xuanjing Huang, Zhongyu Wei

TaCube: Pre-computing Data Cubes for Answering Numerical-Reasoning Questions over Tabular Data (2022.emnlp-main)

Challenge:	We introduce AI-Press, an automated news drafting and polishing system based on multi-agent collaboration and Retrieval-Augmented Generation.
Approach:	They introduce AI-Press, an automated news drafting and polishing system based on multi-agent collaboration and Retrieval-Augmented Generation.
Outcome:	The proposed system generates public responses considering demographic distributions.

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Fan Zhou, Mengkang Hu, Haoyu Dong, Zhoujun Cheng, Fan Cheng, Shi Han, Dongmei Zhang

Reasoning

Challenge:	Existing auto-regressive pre-trained language models are challenged by recent emerging numerical reasoning datasets due to the error-prone implicit calculation.
Approach:	They propose a pre-computation tool to pre-compute aggregation/arithmetic results for the table in advance, so they are handy and readily available for PLMs to answer numerical reasoning questions.
Outcome:	The proposed model improves on TAT-QA and T5 and BART-large on multiple benchmarks.

Logic Rules as Explanations for Legal Case Retrieval (2024.lrec-main)

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ZhongXiang Sun, Kepu Zhang, Weijie Yu, Haoyu Wang, Jun Xu

Explainability & Interpretability in NLP

Challenge:	Recent efforts to learn explainable legal case retrieval models fail to provide faithful and interpretable explanations for legal cases.
Approach:	They propose a framework that uses logic rules to explain legal case retrieval results . they extend benchmarks of LeCaRD and ELAM with manually annotated logic rules .
Outcome:	The proposed framework is able to provide faithful explanations for legal case retrieval.

AutoSchemaKG: Autonomous Knowledge Graph Construction through Dynamic Schema Induction from Web-Scale Corpora (2026.acl-long)

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Jiaxin Bai, Wei Fan, Qi Hu, Qing Zong, Chunyang Li, Hong Ting Tsang, Hongyu Luo, Yauwai Yim, Haoyu Huang, Xiao Zhou, Feng Qin, Tianshi Zheng, Xi Peng, Xin Yao, Huiwen Yang, Leijie Wu, JI Yi, Gong Zhang, Renhai Chen, Yangqiu Song

SoMeLVLM: A Large Vision Language Model for Social Media Processing (2024.findings-acl)

Challenge:	Existing knowledge graph construction frameworks require predefined schemas, limiting their scalability and domain coverage.
Approach:	They propose a framework for fully autonomous knowledge graph construction that eliminates the need for predefined schemas.
Outcome:	The proposed framework outperforms state-of-the-art models on multi-hop QA tasks and enhances LLM factuality.

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Xinnong Zhang, Haoyu Kuang, Xinyi Mou, Hanjia Lyu, Kun Wu, Siming Chen, Jiebo Luo, Xuanjing Huang, Zhongyu Wei

Joint Constrained Learning for Event-Event Relation Extraction (2020.emnlp-main)

Challenge:	Genereal domain large models lack nuanced multimodal understanding of social media . general domain models focus more on text than other modalities, which is not consistent with real-world user habits.
Approach:	They propose a Large Vision Language Model for Social Media Processing that combines five key capabilities to understand and generate real social media behavior.
Outcome:	The proposed model achieves state-of-the-art performance in multiple social media tasks.

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Haoyu Wang, Muhao Chen, Hongming Zhang, Dan Roth

Complex Question Decomposition for Semantic Parsing (P19-1)

Challenge:	Understanding natural language involves recognizing how multiple event mentions structurally and temporally interact with each other.
Approach:	They propose a joint constrained learning framework that enforces logical constraints within and across multiple temporal and subevent relations of events by converting constraints into differentiable learning objectives.
Outcome:	The proposed framework outperforms SOTA methods on benchmarks for temporal relation extraction and event hierarchy construction.

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Haoyu Zhang, Jingjing Cai, Jianjun Xu, Ji Wang

Profile Consistency Identification for Open-domain Dialogue Agents (2020.emnlp-main)

Challenge:	Existing methods that ignore the decompositionality of complex questions are not suitable for complex question semantic parsing.
Approach:	They propose a hierarchical semantic parsing method which utilizes the decompositionality of complex questions for semantic paring.
Outcome:	The proposed method improves on a large scale complex question semantic parsing dataset.

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Haoyu Song, Yan Wang, Wei-Nan Zhang, Zhengyu Zhao, Ting Liu, Xiaojiang Liu

Dialogue Systems & Conversational Agents

Challenge:	Existing studies on improving attribute consistency focus on incorporating attribute information in responses, but few efforts have identified the consistency relations between response and attribute profile.
Approach:	They propose a key-value structure information enriched BERT model to identify the profile consistency . they propose to incorporate attribute information into the generated responses .
Outcome:	The proposed model improves over strong baselines on downstream tasks.

CoV: Chain-of-View Prompting for Spatial Reasoning (2026.findings-acl)

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Haoyu Zhao, Akide Liu, Zeyu Zhang, Weijie Wang, Feng Chen, Ruihan Zhu, Gholamreza Haffari, Bohan Zhuang

Low-Resource NLP

Challenge:	Embodied question answering requires collecting context that is distributed across multiple viewpoints . most recent vision–language models (VLMs) are constrained to a fixed and finite set of input views .
Approach:	They propose a training-free, test-time reasoning framework that transforms a VLM into an active viewpoint reasoner through a coarse-to-fine exploration process.
Outcome:	The proposed framework improves LLM-Match performance by 11.98% on four mainstream VLMs.

Learning Constraints and Descriptive Segmentation for Subevent Detection (2021.emnlp-main)

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Haoyu Wang, Hongming Zhang, Muhao Chen, Dan Roth

Challenge:	Event mentions in text correspond to real-world events of varying degrees of granularity . task of subevent detection aims to resolve this granulem issue by recognizing membership of events .
Approach:	They propose a task of event-based text segmentation as an auxiliary task to improve learning for subevent detection.
Outcome:	The proposed method outperforms baseline methods on subevent detection, HiEve and IC datasets while achieving decent performance on EventSeg prediction.

HiTab: A Hierarchical Table Dataset for Question Answering and Natural Language Generation (2022.acl-long)

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Zhoujun Cheng, Haoyu Dong, Zhiruo Wang, Ran Jia, Jiaqi Guo, Yan Gao, Shi Han, Jian-Guang Lou, Dongmei Zhang

Natural Language Interfaces

Challenge:	Existing studies on table reasoning focus on flat tables and hierarchical tables . a new dataset, HiTab, aims to examine numerical reasoning over hierarchic tables based on hierarchically structured tables - a strong challenge for existing baselines and a valuable benchmark for future research.
Approach:	They propose a hierarchical question answering and natural language generation dataset to study hierarchic tables.
Outcome:	The proposed model shows that it is effective in QA and natural language generation over hierarchical tables.

MSCode: Advancing Human Motion-Language Understanding via Modality-Shared Codebook (2026.findings-acl)

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Haoyu Shi, Huaiwen Zhang

TableLoRA: Low-rank Adaptation on Table Structure Understanding for Large Language Models (2025.acl-long)

Challenge:	Existing methods for motion understanding lack precise alignment between motion and modalities . existing methods lack precise semantics and a mismatch between motion, text .
Approach:	They propose a modality-shared codebook that enables unified representation learning and precise alignment between motion and linguistic modalities.
Outcome:	The proposed model surpasses current state-of-the-art methods in many areas . it enables unified representation learning and precise alignment of motion and modalities .

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Xinyi He, Yihao Liu, Mengyu Zhou, Yeye He, Haoyu Dong, Shi Han, Zejian Yuan, Dongmei Zhang

STAF: Pushing the Boundaries of Test-Time Adaptation towards Practical Noise Scenarios (2024.lrec-main)

Challenge:	Tabular data are crucial in many fields and their understanding by large language models (LLMs) under high parameter efficiency paradigm is important.
Approach:	They propose a module that uses 2D LoRA to encode low-rank information on cell positions to improve table serialization and representation of two-dimensional structured information within a one-dimensional sequence.
Outcome:	Experiments on four tabular-related datasets show that TableLoRA outperforms vanilla LoRA and surpasses table encoding methods tested in control.

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Haoyu Xiong, Xinchun Zhang, Leixin Yang, Yu Xiang, Gang Fang

Responsible & Trustworthy NLP

Challenge:	Pre-trained language models have demonstrated superior performance on NLP tasks . however, when the training domain and testing domain are taken from different distributions, the deployed model often violates this assumption.
Approach:	They propose a Stable Test-time Adaptation Framework to stabilize the adaptation process.
Outcome:	The proposed framework boosts model robustness to noise distribution shifts while minimizing error accumulation and catastrophic forgetting.

Generate, Delete and Rewrite: A Three-Stage Framework for Improving Persona Consistency of Dialogue Generation (2020.acl-main)

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Haoyu Song, Yan Wang, Wei-Nan Zhang, Xiaojiang Liu, Ting Liu

Dialogue Systems & Conversational Agents

Challenge:	Existing persona-based dialogue models generate human-like responses but can hardly avoid the generation of inconsistent persona words.
Approach:	They propose a framework that deletes inconsistent words from a generated response prototype and further rewrites it to a personality-consistent one.
Outcome:	The proposed framework achieves good performance on the persona-chat dataset.

Event Causality Identification with Synthetic Control (2024.emnlp-main)

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Haoyu Wang, Fengze Liu, Jiayao Zhang, Dan Roth, Kyle Richardson

Challenge:	Existing approaches to event causality identification have primarily utilized linguistic patterns and multi-hop relational inference, risking false causality .
Approach:	They propose to use the Rubin Causal Model to identify event causality by generating a twin from existing corpora.
Outcome:	The proposed method can identify causal relations more robustly than previous methods, including GPT-4, which is demonstrated on a causality benchmark, COPES-hard.

Finch: Benchmarking Finance & Accounting across Spreadsheet-Centric Enterprise Workflows (2026.findings-acl)

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Haoyu Dong, Pengkun Zhang, Yan Gao, Xuanyu Dong, Yilin Cheng, Mingzhe Lu, Adina Yakefu, Shuxin Zheng

Analogous Process Structure Induction for Sub-event Sequence Prediction (2020.emnlp-main)

Challenge:	FinWorkBench evaluates real-world enterprise-grade finance and accounting workflows . a human evaluation of GPT 5.1 Pro passes only 38.4% of workflows, a study finds .
Approach:	They propose a workflow construction process that combines LLM-assisted mining and expert annotation to build 172 composite workflows.
Outcome:	The proposed process combines expert annotation with LLM-assisted mining of workflows from authentic enterprise environments.

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Hongming Zhang, Muhao Chen, Haoyu Wang, Yangqiu Song, Dan Roth

Challenge:	Existing work on event understanding is focusing on procedural (or horizontal) tasks such as predicting the next event given an observed sequence.
Approach:	They propose an Analogous Process Structure Induction framework which leverages analogies among processes and conceptualization of sub-event instances to predict the whole sub- sequence of previously unseen open-domain processes.
Outcome:	The proposed framework can predict the whole sub-event sequence of previously unseen open-domain processes.

KET-QA: A Dataset for Knowledge Enhanced Table Question Answering (2024.lrec-main)

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Mengkang Hu, Haoyu Dong, Ping Luo, Shi Han, Dongmei Zhang

Effective In-Context Example Selection through Data Compression (2024.findings-acl)

Challenge:	Existing datasets that ignore the challenge of missing knowledge in TableQA are limited in their use.
Approach:	They propose to use a knowledge base as the external knowledge source for TableQA and construct a dataset with fine-grained gold evidence annotation.
Outcome:	The proposed model achieves remarkable performance improvements on three different settings, but still lags behind the human-level performance.

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ZhongXiang Sun, Kepu Zhang, Haoyu Wang, Xiao Zhang, Jun Xu

FORTAP: Using Formulas for Numerical-Reasoning-Aware Table Pretraining (2022.acl-long)

Challenge:	In-context learning has been validated in large language models, but the mechanism and selection strategy for in-cont example selection lacks systematic and in-depth research.
Approach:	They propose a data compression approach to select in-context examples using large language models.
Outcome:	The proposed method shows a significant improvement of 5.90% across five real-world datasets using four language models.

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Zhoujun Cheng, Haoyu Dong, Ran Jia, Pengfei Wu, Shi Han, Fan Cheng, Dongmei Zhang

Reasoning

Challenge:	Tables store rich numerical data, but numerical reasoning over tables is still a challenge.
Approach:	They propose a spreadsheet formula is a valuable supervision for numerical reasoning in tables.
Outcome:	The proposed method outperforms state-of-the-art methods on three representative datasets of formula prediction, question answering, and cell type classification.

TablePilot: Recommending Human-Preferred Tabular Data Analysis with Large Language Models (2025.acl-industry)

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Deyin Yi, Yihao Liu, Lang Cao, Mengyu Zhou, Haoyu Dong, Shi Han, Dongmei Zhang

Zero-shot Label-Aware Event Trigger and Argument Classification (2021.findings-acl)

Challenge:	Tabular data analysis is crucial in many scenarios, yet its complexity and density can make it challenging to determine the most appropriate analysis operations for a new table.
Approach:	They propose a tabular data analysis framework that recommends query-code-result triplets for new tables . they propose Rec-Align, a method to further improve recommendation quality .
Outcome:	The proposed framework achieves 77.0% top-5 recommendation recall on a dataset designed for tabular data analysis recommendation.

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Hongming Zhang, Haoyu Wang, Dan Roth

PreGenie: An Agentic Framework for High-quality Visual Presentation Generation (2025.findings-emnlp)

Challenge:	Existing work on event extraction relies on labor-intensive annotation, ignoring semantic meaning of event types' labels.
Approach:	They propose a zero-shot event extraction approach that first identifies events with existing tools and then maps them to a given taxonomy of event types in a no-shot manner.
Outcome:	The proposed approach doubles the performance of previous approaches on a ACE-2005 dataset . it leverages label representations induced by pre-trained language models and maps events to the target types .

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Xiaojie Xu, Xinli Xu, Sirui Chen, Haoyu Chen, Fan Zhang, Ying-Cong Chen

Extracting or Guessing? Improving Faithfulness of Event Temporal Relation Extraction (2023.eacl-main)

Challenge:	Visual presentations are vital for effective communication, but they are limited by their complexity and lack of visual understanding.
Approach:	a new framework is proposed to generate high-quality visual presentations using multimodal large language models.
Outcome:	The proposed framework outperforms existing models in multimodal understanding and content consistency.

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Haoyu Wang, Hongming Zhang, Yuqian Deng, Jacob Gardner, Dan Roth, Muhao Chen

Example Quality Matters: Multi-Aspects Example Augmentation for Private Library Programming (2026.acl-long)

Challenge:	Existing models for event temporal relation extraction are based on data-driven machine learning . however, TEMPREL extraction is not accurate under distribution shifts.
Approach:	They propose to conduct counterfactual analysis to attenuate the effects of two types of training biases: the event trigger bias and the frequent label bias.
Outcome:	The proposed model extracts TempRel and timelines more faithfully compared to SOTA methods . it is based on two perspectives: one is to extract genuinely based upon contextual description . the other is to provide proper uncertainty estimation and abstain from extraction when no relation is described in the text .

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Yuhao Li, Haifeng Sun, Xuesong Zhang, Shu Yao, Haoyu Zheng, Yvchuan Wang, Huazheng Wang, Zirui Zhuang, Qi Qi, Jianxin Liao, Jingyu Wang

Responsible & Trustworthy NLP

Challenge:	Existing approaches to code generation fail to consider the quality of retrieved examples.
Approach:	They propose a retrieval-augmented generation method that combines existing API examples to improve complexity and readability.
Outcome:	The proposed method achieves up to 22% accuracy improvement over baseline methods.

NL2Formula: Generating Spreadsheet Formulas from Natural Language Queries (2024.findings-eacl)

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Wei Zhao, Zhitao Hou, Siyuan Wu, Yan Gao, Haoyu Dong, Yao Wan, Hongyu Zhang, Yulei Sui, Haidong Zhang