Papers by Zhipeng Wang
Distilling the Essence: Efficient Reasoning Distillation via Sequence Truncation (2026.findings-acl)
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Wei-Rui Chen, Vignesh Kothapalli, Ata Fatahibaarzi, Hejian Sang, Shao Tang, Qingquan Song, Zhipeng Wang, Muhammad Abdul-Mageed
| Challenge: | a large reasoning model (LRM) training on large amounts of reasoning data is computationally expensive. |
| Approach: | They propose a method to quantify computation-quality tradeoffs as a function of sequence length. |
| Outcome: | The proposed method reduces training time, memory and FLOPs by 50% on long training sequences while retaining the full-sequence performance. |
Paper2Rebuttal: A Multi-Agent Framework for Transparent Author Response Assistance (2026.acl-long)
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| Challenge: | Current approaches to writing effective rebuttals are limited by the direct-to-text generation problem . authors must accurately decipher reviewer intent while ensuring every response is firmly anchored in verifiable manuscript details. |
| Approach: | They propose a framework that reframes rebuttal generation as an evidence-centric planning task. |
| Outcome: | The proposed framework outperforms baselines in coverage, faithfulness, and strategic coherence. |
CogKGE: A Knowledge Graph Embedding Toolkit and Benchmark for Representing Multi-source and Heterogeneous Knowledge (2022.acl-demo)
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Zhuoran Jin, Tianyi Men, Hongbang Yuan, Zhitao He, Dianbo Sui, Chenhao Wang, Zhipeng Xue, Yubo Chen, Jun Zhao
| Challenge: | Existing methods focus on entity-centric knowledge, but CogKGE supports heterogeneous knowledge. |
| Approach: | They propose a knowledge graph embedding toolkit to represent multi-source and heterogeneous knowledge. |
| Outcome: | The proposed toolkit provides a unified programming framework for KGE tasks and a series of knowledge representations for downstream tasks. |
Dataset for the First Evaluation on Chinese Machine Reading Comprehension (L18-1)
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| Challenge: | Existing reading comprehension datasets are mostly in English . |
| Approach: | They propose a Chinese reading comprehension dataset to add diversity to existing reading comprehension data . proposed dataset contains cloze-style reading comprehension and user query reading comprehension . |
| Outcome: | The proposed dataset is based on a Chinese reading comprehension dataset . it includes two types of cloze-style and user query reading comprehension . the proposed dataset hosted the 1st Evaluation on Chinese Machine Reading Comprehension (CMRC-2017) |
A Sentence Cloze Dataset for Chinese Machine Reading Comprehension (2020.coling-main)
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| Challenge: | Using cloze-style reading comprehension, Chinese machine reading comprehension datasets are becoming more and more popular . a new task is proposed to fill the right candidate sentence into the passage with several blanks . |
| Approach: | They propose a Chinese task to fill the right candidate sentence into a passage with blanks . they build a dataset to evaluate the difficulty of the task and make fake candidates . |
| Outcome: | The proposed task fills the right candidate sentence into the passage with blanks . the proposed dataset contains over 100K blanks within over 10K passages based on Chinese narrative stories . |
TextBrewer: An Open-Source Knowledge Distillation Toolkit for Natural Language Processing (2020.acl-demos)
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| Challenge: | Large pre-trained language models have hundreds of millions of parameters and take several gigabytes of memory to train and inference. |
| Approach: | They propose an open-source knowledge distillation toolkit designed for natural language processing that provides a set of predefined distillation methods and can be extended with custom code. |
| Outcome: | The proposed method is comparable with or even higher than the public distilled BERT models with similar numbers of parameters. |
ThinkNote: Enhancing Knowledge Integration and Utilization of Large Language Models via Constructivist Cognition Modeling (2026.findings-eacl)
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Zhipeng Xu, Zhenghao Liu, Yukun Yan, Shuo Wang, Shi Yu, Zheni Zeng, Chaojun Xiao, Zhiyuan Liu, Ge Yu, Chenyan Xiong
| Challenge: | Large Language Models (LLMs) exhibit suboptimal behaviors and inconsistencies when exposed to unfamiliar external information, underscoring their limitations in effectively leveraging such knowledge. |
| Approach: | They propose a framework that enhances the external knowledge utilization of Large Language Models through a two-stage constructivist cognitive modeling process. |
| Outcome: | The proposed framework achieves a 10% improvement over baseline methods on various question-answering benchmarks. |
Extracting and Combining Abilities For Building Multi-lingual Ability-enhanced Large Language Models (2025.emnlp-main)
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| Challenge: | Existing work relies on training with multi-lingual ability-related data, which may not be available for low-resource languages. |
| Approach: | They propose a multi-lingual ability-enhanced LLM that extracts language-agnostic ability-related weights from LLMs and combine them across different languages by simple addition and subtraction operations without training. |
| Outcome: | The proposed approach extracts language-agnostic ability-related weights from LLMs and combine them across different languages without training. |
A Span-Extraction Dataset for Chinese Machine Reading Comprehension (D19-1)
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| Challenge: | Existing reading comprehension datasets are mostly in English . MRC is a new field of research that aims to comprehend the context of articles and answer the questions based on them. |
| Approach: | They propose a Span-Extraction dataset for Chinese machine reading comprehension to add language diversities to existing reading comprehension datasets. |
| Outcome: | The proposed dataset is composed of 20,000 real questions annotated on Wikipedia paragraphs by human experts. |
Scaling Down, Serving Fast: Compressing and Deploying Efficient LLMs for Recommendation Systems (2025.emnlp-industry)
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Kayhan Behdin, Ata Fatahibaarzi, Qingquan Song, Yun Dai, Aman Gupta, Zhipeng Wang, Hejian Sang, Shao Tang, Gregory Dexter, Sirou Zhu, Siyu Zhu, Tejas Dharamsi, Vignesh Kothapalli, Zhoutong Fu, Yihan Cao, Pin-Lun Hsu, Fedor Borisyuk, Natesh S. Pillai, Luke Simon, Rahul Mazumder
| Challenge: | Large language models (LLMs) have demonstrated remarkable performance across a wide range of industrial applications. |
| Approach: | They propose two techniques for training and deploying small language models that deliver high performance for a variety of industry use cases. |
| Outcome: | The proposed techniques retain much of the quality of larger models while reducing training/serving costs and latency. |
Towards Effective and Efficient Continual Pre-training of Large Language Models (2025.acl-long)
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Jie Chen, Zhipeng Chen, Jiapeng Wang, Kun Zhou, Yutao Zhu, Jinhao Jiang, Yingqian Min, Xin Zhao, Zhicheng Dou, Jiaxin Mao, Yankai Lin, Ruihua Song, Jun Xu, Xu Chen, Rui Yan, Zhewei Wei, Di Hu, Wenbing Huang, Ji-Rong Wen
| Challenge: | Continual pre-training (CPT) has been an important approach for adapting language models to specific domains or tasks. |
| Approach: | They propose a Continual pre-training method that can greatly improve Chinese language ability and scientific reasoning ability of LLMs. |
| Outcome: | The proposed method can greatly improve Chinese language ability and scientific reasoning ability of LLMs. |
LEAF: Large Language Diffusion Model for Time Series Forecasting (2025.findings-emnlp)
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| Challenge: | Recent work has applied large language models (LLMs) into time series forecasting, but they lack an understanding of holistic temporal patterns with potential error accumulation. |
| Approach: | They propose a framework that marries Larg e Langu age Diffusion Model with time series forecasting (LEAF) they propose converting time series into tokens and adopting language diffusion models to capture temporal dependencies. |
| Outcome: | The proposed framework generates future predictions with a diffusion model from a holistic view. |
UCTG: A Unified Controllable Text Generation Framework for Query Auto-Completion (2025.coling-industry)
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| Challenge: | Existing approaches to control text generation (CTG) are essentially challenging to adapt to various control objectives and constraints, which results in mixed success. |
| Approach: | They propose a unified controllable text generation framework which integrates a control module, a prompt module, and a generation module. |
| Outcome: | The proposed framework significantly improves query accuracy and coherence in tasks with different objectives and constraints. |
AHA: Aligning Large Audio-Language Models for Reasoning Hallucinations via Counterfactual Hard Negatives (2026.findings-acl)
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Yanxi Chen, Wenhui Zhu, Xiwen Chen, Zhipeng Wang, Xin Li, Peijie Qiu, Hao Wang, Xuanzhao Dong, Yujian Xiong, Anderson Schneider, Yuriy Nevmyvaka, Yalin Wang
| Challenge: | Large Audio-Language Models suffer from hallucinations, e.g., generating text not grounded in the audio input. |
| Approach: | They propose a framework to address hallucination problems in large audio-language models . they use a preference dataset to test the model's accuracy . |
| Outcome: | The proposed model outperforms the latest SOTA methods in terms of performance and generalization. |
ICG: Improving Cover Image Generation via MLLM-based Prompting and Personalized Preference Alignment (2025.emnlp-main)
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Zhipeng Bian, Jieming Zhu, Qijiong Liu, Wang Lin, Guohao Cai, Zhaocheng Du, Jiacheng Sun, Zhou Zhao, Zhenhua Dong
| Challenge: | Large language models and diffusion models have opened new possibilities for AI-generated content . personalized cover image generation remains underexplored despite its critical role in boosting user engagement on digital platforms. |
| Approach: | They propose a framework that integrates MLLM-based prompting with personalized preference alignment to generate high-quality, contextually relevant covers. |
| Outcome: | The proposed framework improves image quality, semantic fidelity, and personalization, leading to stronger user appeal and offline recommendation accuracy in downstream tasks. |
You Only Need One Single Token to Refine Safety Alignment (2026.findings-acl)
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| Challenge: | Excessive safety can lead to over-refusal, where models reject harmful-looking yet benign queries, severely limiting utility. |
| Approach: | They propose a lightweight training-based approach that reshapes the distributions of harmful and benign samples within the model’s decision space by using a single-token prefix. |
| Outcome: | The proposed approach can distinguish between harmful and benign samples while keeping the model frozen. |
CogIE: An Information Extraction Toolkit for Bridging Texts and CogNet (2021.acl-demo)
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| Challenge: | CogNet is a knowledge base that integrates three types of knowledge: linguistic knowledge, world knowledge and commonsense knowledge. |
| Approach: | They propose an information extraction toolkit called CogIE that is a bridge connecting raw texts and CogNet. |
| Outcome: | The proposed toolkit can ground raw texts to CogNet and leverage different types of knowledge to enrich extracted results. |
Lang2Act: Fine-Grained Visual Reasoning through Self-Emergent Linguistic Toolchains (2026.findings-acl)
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| Challenge: | Existing frameworks depend on rigid, pre-defined external tools to extend perceptual capabilities of VLMs. |
| Approach: | They propose a framework that leverages self-emergent linguistic toolchains to enhance visual perception and reasoning. |
| Outcome: | The proposed framework improves the visual perception capabilities of large language models by incorporating external visual documents to address a given query. |
Human-Agent Collaborative Paper-to-Page Crafting (2026.findings-acl)
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Qianli Ma, Siyu Wang, Chen Yilin, Yinhao Tang, Yixiang Yang, Chang Guo, Bingjie Gao, Zhening Xing, Yanan Sun, Zhipeng Zhang
| Challenge: | Existing approaches to create project pages from academic papers have focused on static slides and posters, but the dynamic nature of webpages remains an unaddressed challenge. |
| Approach: | They propose a novel multi-agent system that deconstructs paper-to-page creation into a coarse-to fine pipeline from narrative planning to multimodal content generation and interactive rendering. |
| Outcome: | The proposed system generates high-quality, visually appealing pages in under 15 minutes for less than $0.1 . |
Crisp: Cognitive Restructuring of Negative Thoughts through Multi-turn Supportive Dialogues (2025.emnlp-main)
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Jinfeng Zhou, Yuxuan Chen, Jianing Yin, Yongkang Huang, Yihan Shi, Xikun Zhang, Libiao Peng, Rongsheng Zhang, Tangjie Lv, Zhipeng Hu, Hongning Wang, Minlie Huang
| Challenge: | Existing approaches to cognitive restructuring (CR) are limited by entrenched cognitive distortions, emotional resistance, and individual differences. |
| Approach: | They propose a framework that structures CR as theory-grounded multi-stage multi-turn dialogue and a multi-channel loop mechanism to account for diverse individual distortions. |
| Outcome: | The proposed framework integrates supportive strategies for emotional management and a multi-channel loop mechanism to account for diverse individual distortions. |
Not Everything is All You Need: Toward Low-Redundant Optimization for Large Language Model Alignment (2024.emnlp-main)
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| Challenge: | Experimental results show that large language models are struggling to align with human preference in complex tasks and scenarios. |
| Approach: | They propose a low-redundant alignment method that selects the top-10% most updated parameters in LLMs for alignment training. |
| Outcome: | The proposed method improves on 10 datasets and shows that it is redundant . it can be used to train LLMs on QA and ECQA datasets, but it is not feasible to test it on a large dataset. |
ElitePLM: An Empirical Study on General Language Ability Evaluation of Pretrained Language Models (2022.naacl-main)
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Junyi Li, Tianyi Tang, Zheng Gong, Lixin Yang, Zhuohao Yu, Zhipeng Chen, Jingyuan Wang, Xin Zhao, Ji-Rong Wen
| Challenge: | Recent years have featured a trend towards Transformer based pretrained language models (PLMs) in natural language processing systems. |
| Approach: | They propose to use four evaluation dimensions to evaluate ten widely-used PLMs . they find that pretrained language models are good at different ability tests . |
| Outcome: | The results show that pretrained language models are good at different ability tests and have excellent transferability between tasks. |
CoDA: Restoring Contextual Dominance via Copy-Encouraged Attention Intervention for Mitigating RAG Hallucinations (2026.findings-acl)
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| Challenge: | Retrieval-augmented generation reduces hallucination by grounding outputs in external evidence. |
| Approach: | They propose a lightweight inference-time attention intervention that amplifies evidence-aligned value states to enhance contextual faithfulness and reduce hallucination. |
| Outcome: | The proposed model reduces hallucination by grounding model outputs in external evidence. |