Papers by Haoyu Dong
Encoding Spreadsheets for Large Language Models (2024.emnlp-main)
Copied to clipboard
Haoyu Dong, Jianbo Zhao, Yuzhang Tian, Junyu Xiong, Mengyu Zhou, Yun Lin, José Cambronero, Yeye He, Shi Han, Dongmei Zhang
| 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. |
EmoMM: Benchmarking and Steering MLLM for Multimodal Emotion Recognition under Conflict and Missingness (2026.findings-acl)
Copied to clipboard
| 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. |
HermEs: Interactive Spreadsheet Formula Prediction via Hierarchical Formulet Expansion (2023.acl-long)
Copied to clipboard
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. |
Event Semantic Classification in Context (2024.findings-eacl)
Copied to clipboard
| 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. |
MMedAgent: Learning to Use Medical Tools with Multi-modal Agent (2024.findings-emnlp)
Copied to clipboard
Binxu Li, Tiankai Yan, Yuanting Pan, Jie Luo, Ruiyang Ji, Jiayuan Ding, Zhe Xu, Shilong Liu, Haoyu Dong, Zihao Lin, Yixin Wang
| Challenge: | Multi-modal Large Language Models (MLLMs) exhibit limited generality and often fall short when compared to specialized models. |
| Approach: | They propose a multi-modal medical agent that picks the most suitable medical tools based on user inputs. |
| Outcome: | The proposed agent performs better than open-source models and the closed-source model, GPT-4o. |
CLIP Models are Few-Shot Learners: Empirical Studies on VQA and Visual Entailment (2022.acl-long)
Copied to clipboard
| 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. |
RelationalCoder: Rethinking Complex Tables via Programmatic Relational Transformation (2025.acl-long)
Copied to clipboard
| Challenge: | Semi-structured tables remain a major obstacle for automated data processing and analytics. |
| Approach: | They propose a technique called Loop Reference Decoding which identifies expandable groups and replicates each group using a concise loop over its repetitive region. |
| Outcome: | The proposed technique reduces output length from O(N M) to approximately O(K) Extensive experiments on HiTab and MultiHiertt show that it boosts Llama-2 and Mistral models by more than 20%, and GPT-4o by over 4%. |
Table-LLM-Specialist: Language Model Specialists for Tables using Iterative Fine-tuning (2025.emnlp-main)
Copied to clipboard
| 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. |
PLOG: Table-to-Logic Pretraining for Logical Table-to-Text Generation (2022.emnlp-main)
Copied to clipboard
| 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)
Copied to clipboard
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
| 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. |
Not All Tokens Matter: Towards Efficient LLM Reasoning via Token Significance in Reinforcement Learning (2026.acl-long)
Copied to clipboard
| 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)
Copied to clipboard
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. |
TableCoder: Table Extraction from Text via Reliable Code Generation (2025.acl-industry)
Copied to clipboard
| Challenge: | Structured table extraction from unstructured text is critical for automating data processing tasks across industries where accuracy and reliability are paramount. |
| Approach: | They propose a natural language-based method for extracting structured tables from text . they use Python classes or SQL statements to explicitly construct table structures . |
| Outcome: | The proposed method improves F1 scores and mitigates hallucinations . it integrates with standard SQL databases and Python workflows, ensuring seamless deployment . |
Representation Interventions Enable Lifelong Knowledge Memory Control in LLMs (2026.acl-long)
Copied to clipboard
Xuyuan Liu, Shengyu Chen, Xinshuai Dong, Yanchi Liu, Xujiang Zhao, Haoyu Wang, Yujun Yan, Haifeng Chen, Zhengzhang Chen
| Challenge: | Large language models (LLMs) produce outdated or inaccurate content. Updating their knowledge efficiently and accurately without costly retraining is a major challenge. |
| Approach: | They propose a robust and scalable method that treats knowledge control as interventions within the model’s representation space. |
| Outcome: | The proposed method achieves fine-grained control over complex, unstructured knowledge while maintaining general utility with frozen base weights. |
TaCube: Pre-computing Data Cubes for Answering Numerical-Reasoning Questions over Tabular Data (2022.emnlp-main)
Copied to clipboard
| 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. |
HiTab: A Hierarchical Table Dataset for Question Answering and Natural Language Generation (2022.acl-long)
Copied to clipboard
Zhoujun Cheng, Haoyu Dong, Zhiruo Wang, Ran Jia, Jiaqi Guo, Yan Gao, Shi Han, Jian-Guang Lou, Dongmei Zhang
| 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. |
TableLoRA: Low-rank Adaptation on Table Structure Understanding for Large Language Models (2025.acl-long)
Copied to clipboard
| 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. |
Finch: Benchmarking Finance & Accounting across Spreadsheet-Centric Enterprise Workflows (2026.findings-acl)
Copied to clipboard
Haoyu Dong, Pengkun Zhang, Yan Gao, Xuanyu Dong, Yilin Cheng, Mingzhe Lu, Adina Yakefu, Shuxin Zheng
| 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. |
KET-QA: A Dataset for Knowledge Enhanced Table Question Answering (2024.lrec-main)
Copied to clipboard
| 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. |
FORTAP: Using Formulas for Numerical-Reasoning-Aware Table Pretraining (2022.acl-long)
Copied to clipboard
| 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)
Copied to clipboard
| 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. |
NL2Formula: Generating Spreadsheet Formulas from Natural Language Queries (2024.findings-eacl)
Copied to clipboard
Wei Zhao, Zhitao Hou, Siyuan Wu, Yan Gao, Haoyu Dong, Yao Wan, Hongyu Zhang, Yulei Sui, Haidong Zhang
| Challenge: | Creating spreadsheet formulas remains a tedious and error-prone task for many end-users . a novel task is proposed to generate spreadsheet formulae from a user's NL query . |
| Approach: | They propose a task to generate formulas that are grounded on a spreadsheet table given a Natural Language query as input. |
| Outcome: | The proposed task generates formulas that are grounded on a spreadsheet table, given a natural language query as input. |