Papers by Yuqi Zhang
AMIA: Automatic Masking and Joint Intention Analysis Makes LVLMs Robust Jailbreak Defenders (2025.findings-emnlp)
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| Challenge: | AMIA is a lightweight, inference-only defense for Large Vision–Language Models . it automatically masks text-irrelevant image patches and conducts joint Intention Analysis . |
| Approach: | AMIA is a lightweight, inference-only defense for large vision–language models . it automatically masks a small set of text-irrelevant image patches to disrupt adversarial perturbations . |
| Outcome: | AMIA improves defense success rates across diverse LVLMs and jailbreak benchmarks . it preserves general utility with only 2% accuracy drop, incurs only modest inference overhead . |
Length-Induced Embedding Collapse in PLM-based Models (2025.acl-long)
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| Challenge: | In text embeddings from PLMs are essential for many NLP applications, but performance degrades on longer texts. |
| Approach: | They propose a method which mitigates the phenomenon of Length Collapse . they propose TempScale to ensure more consistent embeddings across different text lengths . |
| Outcome: | The proposed method improves performance on MTEB and LongEmbed by 0.94% on short and 1.10% on long texts. |
LightThinker: Thinking Step-by-Step Compression (2025.emnlp-main)
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Jintian Zhang, Yuqi Zhu, Mengshu Sun, Yujie Luo, Shuofei Qiao, Lun Du, Da Zheng, Huajun Chen, Ningyu Zhang
| Challenge: | Recent advances in Large Language Models have demonstrated their remarkable capabilities in complex reasoning tasks, but their efficiency is hindered by the substantial memory and computational costs associated with generating lengthy tokens. |
| Approach: | They propose a method that dynamically compresses verbose thought steps into compact representations and discards original reasoning chains. |
| Outcome: | The proposed method reduces peak memory usage and inference time while maintaining competitive accuracy. |
What Makes AI Research Replicable? Executable Knowledge Graphs as Scientific Knowledge Representations (2026.acl-short)
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Yujie Luo, Zhuoyun Yu, Xuehai Wang, Yuqi Zhu, Ningyu Zhang, Lanning Wei, Lun Du, Da Zheng, Huajun Chen
| Challenge: | Existing approaches to replicate AI research are limited by insufficient background knowledge and the limitations of retrieval-augmented generation methods. |
| Approach: | They propose a pluggable, paper-centric knowledge base that integrates code snippets and technical insights extracted from scientific literature into a verifiable, executable representation. |
| Outcome: | The proposed knowledge base shows significant performance gains on paperBench when integrated into three agent frameworks with two different LLMs. |
ExpertIVS: Sociological Expert Driven Individual Value Simulation in Large Language Models (2026.findings-acl)
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Zhen Wang, Yuqi Ren, Yuehan Cui, Hongxiang Wang, Jianxiang Peng, Zhaoxia Zhang, Bingkun Zhu, Tongxuan Zhang, Dezhi Tong, Deyi Xiong
| Challenge: | Existing methods for social simulations mechanically stitch survey responses into prompts, which suffer from semantic fragmentation, failing to capture the internal coherence of human value systems. |
| Approach: | They propose a framework employing 14 Sociological Expert Agents to interpret World Values Survey responses through structured professional perspectives rather than direct responses concatenation. |
| Outcome: | Experiments on 480 individuals from 12 countries show that ExpertIVS outperforms baselines in value generalization and significantly outperfies the existing methods. |
DevEval: A Manually-Annotated Code Generation Benchmark Aligned with Real-World Code Repositories (2024.findings-acl)
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Jia Li, Ge Li, Yunfei Zhao, Yongmin Li, Huanyu Liu, Hao Zhu, Lecheng Wang, Kaibo Liu, Zheng Fang, Lanshen Wang, Jiazheng Ding, Xuanming Zhang, Yuqi Zhu, Yihong Dong, Zhi Jin, Binhua Li, Fei Huang, Yongbin Li, Bin Gu, Mengfei Yang
| Challenge: | Existing benchmarks are poorly aligned with real-world code repositories and are insufficient to evaluate the coding abilities of Large Language Models (LLMs). |
| Approach: | They propose a repository-level benchmark named DevEval to evaluate LLMs' coding abilities in real-world code repositories. |
| Outcome: | The proposed benchmarks show that the LLMs perform better in real-world code repositories than existing benchmarks. |
Intention Analysis Makes LLMs A Good Jailbreak Defender (2025.coling-main)
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| Challenge: | Existing methods to align large language models with human values overlook the intrinsic nature of jailbreaks, which limits their effectiveness in complex scenarios. |
| Approach: | They propose a simple yet highly effective defense strategy, i.e., Intention Analysis (IA). They show that IA suppresses LLM’s tendency to follow jailbreak prompts, thereby enhancing safety. |
| Outcome: | The proposed strategy reduces harmfulness of LLMs and outperforms GPT-3.5 in attack success rate. |
SciAssess: Benchmarking LLM Proficiency in Scientific Literature Analysis (2025.findings-naacl)
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Hengxing Cai, Xiaochen Cai, Junhan Chang, Sihang Li, Lin Yao, Wang Changxin, Zhifeng Gao, Hongshuai Wang, Li Yongge, Mujie Lin, Shuwen Yang, Jiankun Wang, Mingjun Xu, Jin Huang, Xi Fang, Jiaxi Zhuang, Yuqi Yin, Yaqi Li, Changhong Chen, Zheng Cheng, Zifeng Zhao, Linfeng Zhang, Guolin Ke
| Challenge: | Existing benchmarks fail to adequately evaluate the proficiency of Large Language Models (LLMs) Existing standards do not cover the skills needed to evaluate LLMs in scientific literature analysis. |
| Approach: | They propose a benchmark to evaluate the proficiency of large language models in scientific literature analysis. |
| Outcome: | SciAssess evaluates 11 LLMs on multiple tasks across scientific fields. |
D-Artemis: A Deliberative Cognitive Framework for Mobile GUI Multi-Agents (2026.findings-acl)
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Hongze Mi, Yibo Feng, WenJie Lu, Yuqi Wang, Jinyuan Li, Song Cao, He Cui, Tengfei Tian, Xuelin Zhang, Haotian Luo, Di Sun, Jun Fang, Hua Chai, Naiqiang Tan, Gang Pan
| Challenge: | Graphical User Interface (GUI) agents aim to automate a wide spectrum of human tasks by emulating user interaction. |
| Approach: | They propose a deliberative framework that leverages a fine-grained tip retrieval mechanism to inform its decision-making process. |
| Outcome: | The proposed framework achieves SOTA among open-source general models on AndroidWorld and ScreenSpot-V2 . it leverages a fine-grained, app-specific tip retrieval mechanism to inform its decision-making process . |
Exploring Quality and Diversity in Synthetic Data Generation for Argument Mining (2025.emnlp-main)
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| Challenge: | Argument Mining (AM) is hindered by the scarcity of structure-annotated datasets, which are expensive to create manually. |
| Approach: | They propose to use quality-oriented synthesis and diversity-oriented approach to generate argumentative texts with diverse topics and argument structures. |
| Outcome: | The proposed approach significantly improves existing models in full-data and low-resource settings. |
Task-adaptive Label Dependency Transfer for Few-shot Named Entity Recognition (2023.findings-acl)
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| Challenge: | Existing methods for Named Entity Recognition (NER) ignore label dependency, resulting in suboptimal performance. |
| Approach: | They propose a meta-learning method to make label dependency transferable by learning general initialization and individual parameter update rule for label dependency. |
| Outcome: | The proposed method improves existing methods by learning general initialization and individual parameter update rule for label dependency. |
Multimodal Cross-lingual Phrase Retrieval (2024.lrec-main)
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| Challenge: | Existing approaches to cross-lingual phrase retrieval only deal with textual modality, leaving the question of the effectiveness of using multimodal information unanswered. |
| Approach: | They propose a multimodal cross-lingual phrase retrieval resource that integrates a Wikimedia Commons media store and a large multimodal pre-trained model to bridge the gap between different modalities. |
| Outcome: | The proposed approach performs significantly better than pure textual cross-lingual phrase retrieval on a benchmarked dataset covering eight language pairs. |
Improving Neural Machine Translation by Multi-Knowledge Integration with Prompting (2023.findings-emnlp)
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| Challenge: | Existing methods focus on how to integrate multiple types of knowledge into NMT models . |
| Approach: | They propose a framework that integrates multiple types of knowledge into NMT models . they use multiple types as prefix-prompts of input for the encoder and decoder . |
| Outcome: | The proposed framework outperforms baselines on English-Chinese and English-German translation. |
From Curated Data to Scalable Models: Continual Pre-training of Dense and MoE Large Language Models for Tibetan (2026.acl-long)
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Lei Yang, Leiyu Pan, Bojian Xiong, Renren Jin, Shaowei Zhang, Yue Chen, Ling Shi, Jiang Zhou, Junru Wu, Zhen Wang, Jianxiang Peng, Juesi Xiao, Tianyu Dong, Zhuowen Han, Zhuo Chen, Yuqi Ren, Deyi Xiong
| Challenge: | Large language models have achieved remarkable success across a wide range of tasks, yet their performance remains heavily biased toward high-resource languages. |
| Approach: | They propose a pipeline for advancing Tibetan language modeling through multilingual continual pre-training with Tibetan, Chinese, and English. |
| Outcome: | The proposed model outperforms open-source and Tibetan-focused models on diverse tasks. |
Third-Party Aligner for Neural Word Alignments (2022.findings-emnlp)
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| Challenge: | Existing work shows that word alignment can be competitive . |
| Approach: | They propose to use word alignments generated by a third-party word aligner to supervise the neural word alignment training. |
| Outcome: | The proposed approach can find more accurate word alignments and delete wrong alignments, leading to better performance than the current best third-party word aligner. |
Revisiting Entropy in Reinforcement Learning for Large Reasoning Models (2026.findings-acl)
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Renren Jin, Pengzhi Gao, Yuqi Ren, Zhuowen Han, Tongxuan Zhang, Wuwei Huang, Wei Liu, Jian Luan, Deyi Xiong
| Challenge: | Reinforcement learning with verifiable rewards (RLVR) has emerged as a paradigm for enhancing the reasoning capabilities of large language models. |
| Approach: | They propose a positive-advantage reweighting approach that regulates model entropy by adjusting the loss weights assigned to tokens with positive advantages during RLVR training. |
| Outcome: | The proposed approach regulates model entropy by adjusting loss weights assigned to tokens with positive advantages during RLVR training while maintaining competitive performance. |
Benchmarking Long-Context Language Models on Long Code Understanding (2025.acl-long)
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Jia Li, Xuyuan Guo, Lei Li, Kechi Zhang, Ge Li, Jia Li, Zhengwei Tao, Fang Liu, Chongyang Tao, Yuqi Zhu, Zhi Jin
| Challenge: | Currently, long-context language models are limited by the lack of a rigorous evaluation framework for long code understanding. |
| Approach: | They propose to use a long code understanding benchmark LongCodeU to evaluate LCLMs' long code comprehension ability for practical applications. |
| Outcome: | The proposed benchmarks show that current LCLMs are limited in their long code understanding ability, particularly when the long code length is greater than 32K, falling far short of their claimed 128K to 1M context windows. |
Argus: Benchmarking and Enhancing Vision-Language Models for 3D Radiology Report Generation (2025.findings-acl)
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| Challenge: | Existing work on 3D radiograph report generation focuses on 2D images, but 3D medical images provide more comprehensive diagnostic information. |
| Approach: | They propose a comprehensive training recipe for building high-performing VLMs for 3DRRG using a publicly available 3D CT-report dataset. |
| Outcome: | The proposed model achieves superior performance across different model sizes and input 3D medical image resolutions. |
A Span-level Bidirectional Network for Aspect Sentiment Triplet Extraction (2022.emnlp-main)
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| Challenge: | Aspect Sentiment Triplet Extraction (ASTE) is a new fine-grained sentiment analysis task . recent studies have focused on solving aspects term extraction, opinion term extraction and aspect-level sentiment classification tasks individually or in combination of two subtasks. |
| Approach: | They propose a span-level bidirectional network which utilizes all possible spans as input and extracts triplets from spans bidirectionally. |
| Outcome: | The proposed framework outperforms state-of-the-art methods and improves performance . it can extract triplets of aspect terms, sentiments, and opinion terms from review sentences . |
Do Large Language Models Mirror Cognitive Language Processing? (2025.coling-main)
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| Challenge: | Large language models have demonstrated remarkable abilities in text comprehension and logical reasoning. |
| Approach: | They employ Representational Similarity Analysis to measure alignment between 23 LLMs and fMRI signals of the brain. |
| Outcome: | The results show that training strategies affect the LLM-brain alignment. |
Variator: Accelerating Pre-trained Models with Plug-and-Play Compression Modules (2023.findings-emnlp)
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Chaojun Xiao, Yuqi Luo, Wenbin Zhang, Pengle Zhang, Xu Han, Yankai Lin, Zhengyan Zhang, Ruobing Xie, Zhiyuan Liu, Maosong Sun, Jie Zhou
| Challenge: | Large language models (LLMs) have been successful on NLP tasks but require huge parameter sizes and computational resources. |
| Approach: | They propose a parameter-efficient acceleration method that enhances computational efficiency through plug-and-play compression plugins. |
| Outcome: | The proposed method saves 53% computational costs using only 0.9% additional parameters with a performance drop of less than 2%. |
Beyond Glass-Box Features: Uncertainty Quantification Enhanced Quality Estimation for Neural Machine Translation (2021.findings-emnlp)
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| Challenge: | Quality Estimation (QE) is an essential role in applications of Machine Translation (MT). |
| Approach: | They propose to fuse uncertainty quantification into a pre-trained cross-lingual language model to predict the translation quality. |
| Outcome: | The proposed method achieves state-of-the-art on the datasets of WMT 2020 QE shared task. |
Disambiguated Lexically Constrained Neural Machine Translation (2023.findings-acl)
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| Challenge: | Current approaches to LCNMT assume that pre-specified lexicon constraints are contextually appropriate. |
| Approach: | They propose a framework that disambiguates constraints based on contexts at first and integrates them into LCNMT. |
| Outcome: | The proposed approach outperforms baseline approaches on benchmark datasets and comprehensive experiments in multiple target constraints. |
Easy Guided Decoding in Providing Suggestions for Interactive Machine Translation (2023.acl-long)
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| Challenge: | In order to improve translation efficiency, human translators perform post-editing on machine translations to correct errors. |
| Approach: | They propose to use the parameterized objective function of neural machine translation to deal with the TS problem without additional training. |
| Outcome: | The proposed method improves translation quality by 10.6 BLEU and reduces time overhead by 63.4% on benchmark datasets. |
KnowAgent: Knowledge-Augmented Planning for LLM-Based Agents (2025.findings-naacl)
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Yuqi Zhu, Shuofei Qiao, Yixin Ou, Shumin Deng, Shiwei Lyu, Yue Shen, Lei Liang, Jinjie Gu, Huajun Chen, Ningyu Zhang
| Challenge: | Large Language Models (LLMs) fail to effectively guide the planning trajectories during task solving and result in planning hallucinations. |
| Approach: | They propose a novel approach to enhance the planning capabilities of large language models by incorporating explicit action knowledge. |
| Outcome: | The proposed approach can achieve comparable or superior performance to existing baselines on HotpotQA and ALFWorld. |