Papers by Hang Wu
From Selection to Refinement: Iterative Optimization for Instruction Data (2026.acl-long)
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Hang Hu, Ziyan Liu, Rujie Wen, Ruihui Hou, Xueyan Wu, Mu Zhang, Jianxing Yu, Tong Ruan, Jingping Liu
| Challenge: | Existing methods to optimize instruction tuning datasets face two main challenges: unreasonable pruning of potentially valuable low-quality data and the persistence of noise or semantic drift during revision. |
| Approach: | They propose an automated iterative framework for instruction data optimization that prunes low-quality data and refines low quality data using feedback-driven iteration. |
| Outcome: | The proposed framework outperforms state-of-the-art methods on seven public benchmark datasets with high data efficiency. |
Length Generalization of Causal Transformers without Position Encoding (2024.findings-acl)
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| Challenge: | Besides Transformers without position encodings, the success of NoPE provides a new way to overcome the challenge of generalizing to longer sentences. |
| Approach: | They propose a parameter-efficient tuning for searching attention heads’ best temperature hyper-parameters, which substantially expands NoPE’s context size. |
| Outcome: | The proposed tuning significantly expands NoPE's context size, allowing it to generalize to longer sentences with state-of-the-art generalization algorithms. |
MedAdapter: Efficient Test-Time Adaptation of Large Language Models Towards Medical Reasoning (2024.emnlp-main)
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| Challenge: | Large language models (LLMs) have improved generation and reasoning capabilities compared to traditional BERT-sized models due to massive number of parameters and extensive pre-training on vast textual corpora. |
| Approach: | They propose a unified post-hoc adapter for test-time adaptation of large language models . they propose to fine-tune only a small BERT-sized adapter to rank candidate LLMs . |
| Outcome: | The proposed adapter improves performance on four biomedical tasks without requiring computational resources or sharing data with third parties. |
SynthRL: Scaling Visual Reasoning with Verifiable Data Synthesis (2026.findings-acl)
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| Challenge: | SynthRL synthesizes over 3.3K additional verifiable, challenging questions from approximately 8K seed samples. |
| Approach: | They propose a scalable and guaranteed pipeline for automatic data scaling in reasoning-oriented RL training. |
| Outcome: | The proposed pipeline synthesizes over 3.3K additional verifiable, challenging questions from approximately 8K seed samples. |
EHRAgent: Code Empowers Large Language Models for Few-shot Complex Tabular Reasoning on Electronic Health Records (2024.emnlp-main)
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Wenqi Shi, Ran Xu, Yuchen Zhuang, Yue Yu, Jieyu Zhang, Hang Wu, Yuanda Zhu, Joyce Ho, Carl Yang, May Dongmei Wang
| Challenge: | EHRAgent enables clinicians to interact with EHRs using natural language . reliance on rule-based conversion systems often necessitates additional training or effort from data engineers. |
| Approach: | They propose a large language model agent that generates and executes code in natural language to facilitate clinicians in directly interacting with EHRs. |
| Outcome: | The proposed agent outperforms the strongest baseline by up to 29.6% in success rate on three real-world EHR datasets. |
Towards Robust Few-Shot Relation Classification: Incorporating Relation Description with Agreement (2025.findings-emnlp)
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Mengting Hu, Jianfeng Wu, Ming Jiang, Yalan Xie, Zhunheng Wang, Rui Ying, Xiaoyi Liu, Ruixuan Xu, Hang Gao, Renhong Cheng
| Challenge: | Existing approaches to recognize relational relationships with a few support samples are limited for unlimited queries. |
| Approach: | They propose a simple but effective framework that uses relation descriptions as external knowledge to enhance the model’s comprehension of the relation semantics. |
| Outcome: | The proposed framework outperforms strong baselines while being robust against various NOTA rates. |
BiKT: Enabling Bidirectional Knowledge Transfer Between Pretrained Models and Sequential Downstream Tasks (2024.findings-emnlp)
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| Challenge: | Existing frameworks adapt from initial pretrained model to each downstream task directly, but ignore sequential nature of downstream tasks and feedback effect on pretrained models. |
| Approach: | They propose a framework to enable bidirectional knowledge transfer between pretrained models and downstream tasks in rounds. |
| Outcome: | The proposed framework improves on 9 GLUE datasets and 6 SuperGLUEs. |
Uncertainty-Aware Unlikelihood Learning Improves Generative Aspect Sentiment Quad Prediction (2023.findings-acl)
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| Challenge: | Existing studies focus on what to generate but ignore what not to generate . a template-agnostic method boosts original learning and reduces mistakes simultaneously . |
| Approach: | They propose a template-agnostic method to control the token-level generation . they introduce Monte Carlo dropout to understand the built-in uncertainty of pre-trained language models . |
| Outcome: | The proposed method boosts original learning and reduces mistakes simultaneously on four public datasets. |
Simple but Effective Compound Geometric Operations for Temporal Knowledge Graph Completion (2024.acl-long)
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Rui Ying, Mengting Hu, Jianfeng Wu, Yalan Xie, Xiaoyi Liu, Zhunheng Wang, Ming Jiang, Hang Gao, Linlin Zhang, Renhong Cheng
| Challenge: | Current methods embed factual knowledge into continuous vector space and apply geometric operations to learn potential patterns in temporal knowledge graphs. |
| Approach: | They propose a temporal knowledge graph completion method that uses two geometric operations to learn missing facts in temporal graphs. |
| Outcome: | The proposed method significantly outperforms existing temporal knowledge graph embedding models. |
MUR: Momentum Uncertainty guided Reasoning for Large Language Models (2026.acl-long)
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Hang Yan, Fangzhi Xu, Rongman Xu, Yifei Li, Jian Zhang, Haoran Luo, Xiaobao Wu, Anh Tuan Luu, Haiteng Zhao, Qika Lin, Jun Liu
| Challenge: | Existing methods for optimizing reasoning quality are limited by overthinking. |
| Approach: | They propose a method that allocates thinking budgets to critical reasoning steps by tracking and aggregating step-wise uncertainty over time. |
| Outcome: | The proposed method reduces computation by over 45% on average while improving accuracy by 0.33–3.46%. |
Guardian-as-an-Advisor: Advancing Next-Generation Guardian Models for Trustworthy LLMs (2026.findings-acl)
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Yue Huang, Haomin Zhuang, Jiayi Ye, Han Bao, Yanbo Wang, Hang Hua, Siyuan Wu, Pin-Yu Chen, Xiangliang Zhang
| Challenge: | prevailing taxonomies neglect robustness and honesty, yielding safer-on-paper but less useful systems. |
| Approach: | They propose a soft-gating pipeline where a guardian predicts a binary risk label plus a concise explanation and prepends this advice to the original query for re-inference. |
| Outcome: | The proposed model maintains safety while reducing over-refusal. |
XFormParser: A Simple and Effective Multimodal Multilingual Semi-structured Form Parser (2025.coling-main)
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Xianfu Cheng, Hang Zhang, Jian Yang, Xiang Li, Weixiao Zhou, Fei Liu, Kui Wu, Xiangyuan Guan, Tao Sun, Xianjie Wu, Tongliang Li, Zhoujun Li
| Challenge: | Document AI parsing semi-structured image form is a key information extraction task. |
| Approach: | They propose a multimodal and multilingual semi-structured FORM PARSER which integrates SER and relation extraction into a unified framework. |
| Outcome: | The proposed framework achieves up to 1.79% improvement on RE tasks in multilingual and zero-shot settings. |
Genius: A Generalizable and Purely Unsupervised Self-Training Framework For Advanced Reasoning (2025.acl-long)
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Fangzhi Xu, Hang Yan, Chang Ma, Haiteng Zhao, Qiushi Sun, Kanzhi Cheng, Junxian He, Jun Liu, Zhiyong Wu
| Challenge: | Existing methods for enhancing LLM reasoning rely on supervisory signals . current methods rely heavily on outcome supervision and auxiliary reward models . |
| Approach: | They propose a gen-eralizable and purely unsupervised self-training framework to enhance LLM reasoning without supervision. |
| Outcome: | The proposed framework improves LLM reasoning without supervision without external supervision. |
Text-to-Table: A New Way of Information Extraction (2022.acl-long)
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| Challenge: | Existing methods for information extraction are not well understood . text-to-table is a problem that aims to extract information from text data . |
| Approach: | They propose a new problem setting of information extraction, called text-to-table . they formalize text- to-table as a sequence-tosequence problem . |
| Outcome: | The proposed method outperforms existing methods on text-to-table tasks. |
ImF: Embedding an Implicit Fingerprint in Your Large Language Models (2026.acl-long)
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| Challenge: | Training and serving large language models (LLMs) is resource-intensive, making reliable intellectual property protection and black-box ownership verification increasingly important. |
| Approach: | They propose a method to inject a small set of secret query–response behaviors into model fingerprinting . they encode ownership information into a natural-looking target response and derive a semantically aligned query . |
| Outcome: | The proposed fingerprints improve stealthiness and remain verifiable under model updates and deployment-time prompt interventions. |
GLGE: A New General Language Generation Evaluation Benchmark (2021.findings-acl)
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Dayiheng Liu, Yu Yan, Yeyun Gong, Weizhen Qi, Hang Zhang, Jian Jiao, Weizhu Chen, Jie Fu, Linjun Shou, Ming Gong, Pengcheng Wang, Jiusheng Chen, Daxin Jiang, Jiancheng Lv, Ruofei Zhang, Winnie Wu, Ming Zhou, Nan Duan
| Challenge: | Multi-task benchmarks focus on a range of Natural Language Understanding (NLU) tasks without considering the Natural Language Generation (NLG) models. |
| Approach: | They propose a multi-task benchmark for evaluating the generalization capabilities of NLG models across eight language generation tasks. |
| Outcome: | The proposed benchmarks are based on GLUE and Su-perGLUE for English and several other languages. |
Benchmarking Chinese Commonsense Reasoning of LLMs: From Chinese-Specifics to Reasoning-Memorization Correlations (2024.acl-long)
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| Challenge: | Currently, many benchmarks evaluate the commonsense reasoning of large language models (LLMs), but most are English-based, limiting non-English evaluations. |
| Approach: | They propose to use Chinese commonsense reasoning to evaluate LLMs' commonsensing ability. |
| Outcome: | The proposed benchmark covers both globally known and Chinese-specific commonsense reasoning abilities and can be used as a reference for future research. |
Mathematical Word Problem Generation from Commonsense Knowledge Graph and Equations (2021.emnlp-main)
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| Challenge: | Existing models for generating mathematical word problems are lacking in educational assessment. |
| Approach: | They propose an end-to-end neural model to generate diverse mathematical word problems from commonsense knowledge graph and equations. |
| Outcome: | The proposed model outperforms the SOTA models in terms of evaluation metrics and topic relevance. |
BFS-Prover: Scalable Best-First Tree Search for LLM-based Automatic Theorem Proving (2025.acl-long)
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| Challenge: | Existing approaches to theorem proving in large language models rely on value functions and/or Monte Carlo Tree Search (MCTS), but the potential of simpler methods like Best-First Tree Search remains underexplored. |
| Approach: | They propose a scalable expert iteration framework that implements strategic data filtering at each expert iteration round, excluding problems solvable via beam search node expansion to focus on harder cases. |
| Outcome: | The proposed framework achieves a state-of-the-art score of 72.95 on the MiniF2F test set and challenges the perceived necessity of complex tree search methods. |
Improving Aspect Sentiment Quad Prediction via Template-Order Data Augmentation (2022.emnlp-main)
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| Challenge: | Recent work on aspect sentiment quad prediction (ASQP) uses a template to extract aspect quadruplets from review sentences. |
| Approach: | They propose to use a pre-trained language model to select proper orders from a template order perspective to improve aspect sentiment quad prediction. |
| Outcome: | The proposed method outperforms state-of-the-art methods significantly in low-resource settings. |
ZeroGen: Efficient Zero-shot Learning via Dataset Generation (2022.emnlp-main)
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| Challenge: | Existing approaches to generate training data with pre-trained language models have been found effective in various scenarios. |
| Approach: | They propose an unsupervised zero-shot learning method that generates a dataset from scratch and trains a tiny task model under supervision of the synthesized dataset. |
| Outcome: | The proposed method is annotated-free and efficient, but can provide useful insights from the perspective of data-free model-agnostic knowledge distillation and unreferenced text generation evaluation. |
Adaptive Schema-aware Event Extraction with Retrieval-Augmented Generation (2025.findings-emnlp)
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| Challenge: | Event extraction is a task in natural language processing that involves identifying and extracting event information from unstructured text. |
| Approach: | They propose a paradigm that combines schema paraphrasing with schema retrieval-augmented generation. |
| Outcome: | The proposed paradigm retrieves paraphrased schemas and accurately generates targeted structures. |
DiMo-GUI: Advancing Test-time Scaling in GUI Grounding via Modality-Aware Visual Reasoning (2025.emnlp-main)
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| Challenge: | DiMo-GUI is a training-free framework for GUI grounding that splits input into textual elements and iconic elements, allowing the model to reason over each modality independently using general-purpose vision-language models. |
| Approach: | They propose a training-free framework for GUI grounding that leverages two core strategies: dynamic visual grounding and modality-aware optimization. |
| Outcome: | The proposed framework splits the input into textual elements and iconic elements, allowing the model to reason over each modality independently using general-purpose vision-language models. |
Inhibitory Attacks on Backdoor-based Fingerprinting for Large Language Models (2026.acl-long)
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| Challenge: | Backdoor-based LLM fingerprinting is a promising solution for intellectual property protection . however, the vulnerability of existing LLMs for the ensemble scenario is unexplored . |
| Approach: | They propose two new fingerprinting attack methods to assess the robustness of LLM fingerprinting by token filter attack and sentence verification attack. |
| Outcome: | The proposed methods inhibit the fingerprint response while maintaining ensemble performance. |
𝜙-Decoding: Adaptive Foresight Sampling for Balanced Inference-Time Exploration and Exploitation (2025.acl-long)
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| Challenge: | Existing inference-time optimization strategies address the shortsightedness of auto-regressive generation, but the vast search space leads to excessive exploration and insufficient exploitation. |
| Approach: | They propose a decoding strategy that approximates two distributions via foresight and clustering to provide an efficient estimation of step value. |
| Outcome: | The proposed decoding strategy outperforms strong baselines in performance and efficiency. |
GoViG: Goal-Conditioned Visual Navigation Instruction Generation via Multimodal Reasoning (2026.findings-acl)
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Fengyi Wu, Yifei Dong, Yilong Dai, Guangyu Chen, Qifeng Wu, Huiting Huang, Hang Wang, Qi Dai, Alexander G Hauptmann, Zhi-Qi Cheng
| Challenge: | Current methods for instruction generation depend on privileged inputs such as semantic maps, landmark annotations, and panoramic views. |
| Approach: | They propose a task that generates coherent navigation instructions from egocentric visual observations. |
| Outcome: | The proposed task generates coherent navigation instructions from egocentric visual data . the proposed task improves performance over state-of-the-art methods in BLEU-4 and CIDEr scores . |
CodeIE: Large Code Generation Models are Better Few-Shot Information Extractors (2023.acl-long)
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| Challenge: | Large language models pre-trained on massive corpora have shown impressive few-shot learning ability on many NLP tasks. |
| Approach: | They propose to recast structured output in the form of code instead of natural language and use generative LLMs of code to perform IE tasks. |
| Outcome: | The proposed method outperforms fine-tuning moderate-size pre-trained models and prompting NL-LLMs under few-shot settings. |
ECoK: Emotional Commonsense Knowledge Graph for Mining Emotional Gold (2024.findings-acl)
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Zhunheng Wang, Xiaoyi Liu, Mengting Hu, Rui Ying, Ming Jiang, Jianfeng Wu, Yalan Xie, Hang Gao, Renhong Cheng
| Challenge: | Existing knowledge graphs focus on the representation and reasoning of general factual knowledge, while there are significant deficiencies in the understanding and reasoning for emotional knowledge. |
| Approach: | They propose a commonsense knowledge graph that can be used to represent emotional knowledge by combining theories from psychology, cognitive science, and linguistics. |
| Outcome: | The proposed model surpasses GPT-4-Turbo in the emotion-related tasks. |
CTAL: Pre-training Cross-modal Transformer for Audio-and-Language Representations (2021.emnlp-main)
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| Challenge: | Existing audio-language task-specific predictive approaches focus on building complicated late-fusion mechanisms. |
| Approach: | They propose a cross-modal transformer for audio-and-language that learns inter-modal connections between audio and language through two proxy tasks on a large amount of audio- and-language pairs. |
| Outcome: | The proposed model improves on multiple audio-and-language tasks and can be used in fine-tuning phase. |