Papers by Zhipeng Zhang
Thesis Proposal: Efficient KV Cache Reuse for Multi-Document Retrieval-Augmented Generation (2026.eacl-srw)
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| Challenge: | Retrieval-Augmented Generation (RAG) systems face efficiency bottlenecks in prefill due to attention mechanism, and traditional KV cache only accelerates decoding. |
| Approach: | They propose a multi-document KV cache reuse framework for multi-doc RAG workloads . they propose to resolve position and context misalignment while eliminating document-specific quadratic complexity in prefill. |
| Outcome: | The proposed framework solves position and context misalignment issues while eliminating document-specific quadratic complexity in prefill. |
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. |
AnyTrans: Translate AnyText in the Image with Large Scale Models (2024.findings-emnlp)
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| Challenge: | Recent advances in natural language processing and computer vision have made it possible to translate images with text in one language into equivalent images displaying that text translated into another language. |
| Approach: | They propose an all-encompassing framework for the task–In-Image Machine Translation (IIMT) that incorporates contextual cues from both textual and visual elements during translation. |
| Outcome: | The proposed framework can be constructed using open-source models and requires no training, making it highly accessible and expandable. |
Structure-Aware Zero-Shot Relational Learning for Knowledge Graphs without External Knowledge (2026.findings-acl)
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| Challenge: | Existing methods for Zero-shot Relational Learning depend on external knowledge, resulting in increased annotation costs and limited practical applicability. |
| Approach: | They propose a structure-aware paradigm that performs ZRL without external knowledge . it leverages intrinsic structural patterns in KGs to bridge semantic correlations for new relations with existing ones. |
| Outcome: | The proposed paradigm achieves 10.66% improvement in MRR while reducing annotation costs and enhancing practical applicability on three real-world benchmarks. |
LaMemo: Language Modeling with Look-Ahead Memory (2022.naacl-main)
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| Challenge: | Existing approaches to model long-term dependencies are limited to long texts with thousands of words. |
| Approach: | They propose a look-ahead memory that augments the recurrence memory by attending to the right-side tokens and interpolating with the old memory states to maintain long-term information in the history. |
| Outcome: | Experiments on widely used language modeling benchmarks show that LaMemo outperforms baseline models with recurrence memory. |
DMN: A Compositional Framework for Jailbreaking Multimodal LLMs with Multi-Image Inputs (2026.acl-long)
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| Challenge: | Existing jailbreak methods only use a single image, restricting the attack space . Existing frameworks only use single image to distribute harmful requests across multiple images . |
| Approach: | They propose a compositional jailbreak framework that leverages Distributed instruction, Multimodal evidence and a Number chain task to fully enhance the jailbreak performance. |
| Outcome: | The proposed framework achieves attack success rates of over 90% on GPT-4o, Gemini-2.5-pro and Claude Sonnet 4 . |
UNIKIE-BENCH: Benchmarking Large Multimodal Models for Key Information Extraction in Visual Documents (2026.acl-long)
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Yifan Ji, Zhipeng Xu, Zhenghao Liu, Zulong Chen, Qian Zhang, Zhibo Yang, Junyang Lin, Yu Gu, Ge Yu, Maosong Sun
| Challenge: | Recent Large Multimodal Models (LMMs) have shown promising potential for performing end-to-end KIE directly from document images. |
| Approach: | They propose a benchmark to evaluate the performance of Large Multimodal Models (LMMs) using a constrained-category KIE track and an open-categorical KIE Track. |
| Outcome: | Experiments on 15 state-of-the-art LMMs show performance degradation under diverse schema definitions, long-tail key fields, and complex layouts, along with pronounced performance disparities across different document types and scenarios. |
ChatCoT: Tool-Augmented Chain-of-Thought Reasoning on Chat-based Large Language Models (2023.findings-emnlp)
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| Challenge: | Large language models (LLMs) have excellent performance in evaluation benchmarks, but struggle in complex reasoning tasks. |
| Approach: | They propose a tool-augmented chain-of-thought reasoning framework for chat-based LLMs . they model chain- of-thoughting reasoning as multi-turn conversations to utilize tools . |
| Outcome: | The proposed framework can outperform state-of-the-art models on complex reasoning tasks. |
Jiuge: A Human-Machine Collaborative Chinese Classical Poetry Generation System (P19-3)
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Guo Zhipeng, Xiaoyuan Yi, Maosong Sun, Wenhao Li, Cheng Yang, Jiannan Liang, Huimin Chen, Yuhui Zhang, Ruoyu Li
| Challenge: | Existing systems for automatic poetry generation are model-oriented, resulting in poor user participation. |
| Approach: | They propose a human-machine collaborative Chinese classical poetry generation system called Jiuge . Jiuge allows users to revise unsatisfied parts of a generated poem draft repeatedly . |
| Outcome: | The proposed system allows users to revise unsatisfied parts of a generated poem draft repeatedly. |
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. |
MDS: A Fine-Grained Dataset for Multi-Modal Dialogue Summarization (2024.lrec-main)
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| Challenge: | Summarizing the dialogue into a short message has drawn much attention due to the explosion of various dialogue scenes. |
| Approach: | They develop a multi-modal dialogue summarization dataset to enhance the variety of data available for this research area. |
| Outcome: | The proposed dataset provides a demanding testbed for multi-modal dialogue summarization. |
Improving Large Language Models via Fine-grained Reinforcement Learning with Minimum Editing Constraint (2024.findings-acl)
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| Challenge: | Existing reinforcement learning methods do not provide fine-grained supervision for complex reasoning tasks. |
| Approach: | They propose a reinforcement learning method that incorporates a generative model as the reward model and a token-level supervision model for RL training. |
| Outcome: | Experiments on 8 tasks show the proposed method is effective . |