Papers by Qing Zhu
RETAIL: Towards Real-world Travel Planning for Large Language Models (2025.emnlp-main)
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| Challenge: | Existing travel planning systems assume users provide explicit queries, limiting their practical utility. |
| Approach: | They propose a dataset RETAIL which supports decision-making for implicit queries while covering explicit queries. |
| Outcome: | The proposed model achieves a 1.0% pass rate, suggesting real-world travel planning remains challenging. |
Python is Not Always the Best Choice: Embracing Multilingual Program of Thoughts (2024.emnlp-main)
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Xianzhen Luo, Qingfu Zhu, Zhiming Zhang, Libo Qin, Xuanyu Zhang, Qing Yang, Dongliang Xu, Wanxiang Che
| Challenge: | Program of Thoughts (PoT) is an approach characterized by its executable intermediate steps, which ensure the accuracy of the logical calculations in the reasoning process. |
| Approach: | They propose a task and model agnostic approach which harnesses strength and diversity from various languages to achieve better performance across all tasks. |
| Outcome: | The proposed approach outperforms Python Self-Consistency in almost all tasks and models and achieves comparable or superior performance on ChatGPT. |
CoQuIR: A Comprehensive Benchmark for Code Quality-Aware Information Retrieval (2026.acl-long)
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Jiahui Geng, Fengyu Cai, Shaobo Cui, Qing Li, Liangwei Chen, Chenyang Lyu, Haonan Li, Derui Zhu, Alexander Pretschner, Heinz Koeppl, Fakhri Karray
| Challenge: | Existing benchmarks focus on functional relevance while neglecting code quality. |
| Approach: | They propose a multilingual benchmark to evaluate quality-aware code retrieval . they include fine-grained quality annotations over 42,725 queries and 134,907 code snippets . |
| Outcome: | The proposed benchmarks show that state-of-the-art models fail to separate buggy or insecure code from robust counterparts. |
Make Some Noise: Unlocking Language Model Parallel Inference Capability through Noisy Training (2024.emnlp-main)
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Yixuan Wang, Xianzhen Luo, Fuxuan Wei, Yijun Liu, Qingfu Zhu, Xuanyu Zhang, Qing Yang, Dongliang Xu, Wanxiang Che
| Challenge: | Existing speculative decoding methods require additional model structure and training processes to assist the model for draft token generation. |
| Approach: | They propose a make some noise training framework that introduces some noise at the input for the model to learn the denoising task. |
| Outcome: | The proposed model improves inference speed by 2.3-2.7x times without compromising model performance. |
Modeling Multi-Dimensional Cognitive States in Large Language Models under Cognitive Crowding (2026.findings-acl)
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| Challenge: | Existing Large Language Models (LLMs) mainly address isolated tasks such as emotion analysis or stance detection. |
| Approach: | They propose a large-scale model that combines large-level annotations with hyperbolic space to model human cognitive states. |
| Outcome: | The proposed model outperforms baseline models on cognitive dimensions on single dimension tasks while retaining strong hierarchical structure. |
When Efficiency Becomes a Vulnerability: Computational Cost Attacks on WebAgents (2026.acl-long)
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| Challenge: | Existing WebAgents suffer from computational cost attacks due to long reasoning processes and excessive computational cost. |
| Approach: | They propose a framework that generates adversarial prompts and a reinforcement learning-enhanced selector to identify the most effective perturbations. |
| Outcome: | The proposed framework exploits large language models to generate diverse adversarial prompts and a reinforcement learning–enhanced selector to identify the most effective perturbations. |
Reasoning under Uncertainty: Efficient LLM Inference via Unsupervised Confidence Dilution and Convergent Adaptive Sampling (2025.emnlp-main)
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Zhenning Shi, Yijia Zhu, Yi Xie, Junhan Shi, Guorui Xie, Haotian Zhang, Yong Jiang, Congcong Miao, Qing Li
| Challenge: | Large language models suffer from overconfidence and computational inefficiency due to fixed computation budgets and miscalibrated confidence estimates. |
| Approach: | They propose a framework for computationally efficient, trustworthy reasoning under uncertainty using Diversity-Aware Self-Signal Dilution and Convergent Adaptive Weighted Sampling techniques. |
| Outcome: | The proposed framework reduces inference cost by 70% while maintaining accuracy levels while reducing inference costs. |
Large-Scale Multimodal Knowledge Graph about Classical Chinese Poetry: Fine-grained Method and Comprehensive Evaluation (2026.findings-acl)
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| Challenge: | Existing studies on classical Chinese poetry are limited by modality constraints, dataset size, or the level of refinement. |
| Approach: | They propose to construct a large-scale and fine-grained multimodal knowledge graph of classical Chinese poetry using an informative ontology graph and a text-image alignment method. |
| Outcome: | The proposed method collects knowledge about classical Chinese poetry from ontology graphs and performs four tasks that demonstrate its comprehensiveness and high quality. |
Marco-Bench-MIF: On Multilingual Instruction-Following Capability of Large Language (2025.acl-long)
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Bo Zeng, Chenyang Lyu, Sinuo Liu, Mingyan Zeng, Minghao Wu, Xuanfan Ni, Tianqi Shi, Yu Zhao, Yefeng Liu, Chenyu Zhu, Ruizhe Li, Jiahui Geng, Qing Li, Yu Tong, Longyue Wang, Weihua Luo, Kaifu Zhang
| Challenge: | Existing datasets for instruction-following are monolingual and centered on English . existing data are unable to capture linguistic and cultural subtle differences . |
| Approach: | They propose an extension of IFEval to a localized multilingual version called Marco-Bench-MIF . their benchmark addresses linguistic constraints and cultural references via translation and verification . |
| Outcome: | The proposed extension of IFEval to a localized multilingual version covers 30 languages with varying levels of localization. |
Multi-Granularity History and Entity Similarity Learning for Temporal Knowledge Graph Reasoning (2024.emnlp-main)
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| Challenge: | Existing models for Temporal Knowledge Graph reasoning capture repetitive history, ignoring the entity's multi-hop neighbour history which can provide valuable background knowledge for TKG reasoning. |
| Approach: | They propose a multi-granularity history and entity similarity learning model which captures the similarity between entities. |
| Outcome: | The proposed model can predict unknown facts based on historical information, but most existing models ignore multi-hop neighbour history which can provide valuable background knowledge for TKG reasoning. |
Explanation in the Era of Large Language Models (2024.naacl-tutorials)
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| Challenge: | Explanation has long been a part of communication, where humans use language to elucidate each other and transmit information about mechanisms of events. |
| Approach: | They review the opportunities and challenges of explanations in the era of large language models and examine how they can be used to generate explanations. |
| Outcome: | The proposed methods are based on the models of large language models (LLMs) and their opaque nature. |
SmartTrim: Adaptive Tokens and Attention Pruning for Efficient Vision-Language Models (2024.lrec-main)
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Zekun Wang, Jingchang Chen, Wangchunshu Zhou, Haichao Zhu, Jiafeng Liang, Liping Shan, Ming Liu, Dongliang Xu, Qing Yang, Bing Qin
| Challenge: | Experimental results show that SmartTrim accelerates the original model by 2-3 times with minimal performance degradation. |
| Approach: | They propose an adaptive acceleration framework which prunes redundant token representations and attention heads within each layer of the original model. |
| Outcome: | The proposed framework accelerates the original model by 2-3 times with minimal performance degradation across vision-language tasks. |
Neural Topic Modeling via Contextual and Graph Information Fusion (2025.emnlp-main)
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| Challenge: | Existing topic models generate uninformative and incoherent topics that hinder interpretable insights from managing textual data. |
| Approach: | They propose to incorporate contextual and graph information to improve the variational autoencoder framework by combining contextual and bag-of-words information. |
| Outcome: | The proposed framework generates more coherent and diverse topics on three benchmark datasets and achieves strong performance on automatic and manual evaluations. |
MMAPG: A Training-Free Framework for Multimodal Multi-hop Question Answering via Adaptive Planning Graphs (2025.emnlp-main)
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| Challenge: | Existing multimodal question answering models rely on sequential retrieval and reasoning, but this single-path paradigm makes them vulnerable to errors due to misleading intermediate steps. |
| Approach: | They propose a multimodal multi-hop question answering framework guided by an Adaptive Planning Graph . they propose modality-specific strategies that dynamically adapt to distinct data types . |
| Outcome: | The proposed framework outperforms existing models that rely on training. |
FoldMoE: Efficient Long Sequence MoE Training via Attention-MoE Pipelining (2025.acl-long)
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| Challenge: | Existing approaches to training LLMs with Mixture-of-Experts (MoE) architecture on long sequences are limited by the insufficient computation. |
| Approach: | They propose a MoE training system that enables token-level overlapping across entire Transformer blocks through novel attention-MoE pipelining. |
| Outcome: | The proposed system achieves 1.49x and 2.72x speedup over state-of-the-art token-level overlapping and non-overlapping baselines on GPT-MoE models with sequences up to 32K tokens. |
PoLLMgraph: Unraveling Hallucinations in Large Language Models via State Transition Dynamics (2024.findings-naacl)
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| Challenge: | Existing studies have recognized hallucination as a notable concern in large autoregressive language models (LLMs). |
| Approach: | They propose a polygraph for large language models that detects "hallucination" they demonstrate that hallucination can be detected by tractable probabilistic models . |
| Outcome: | The proposed model outperforms state-of-the-art methods on open-source LLMs by 20% on TruthfulQA benchmarks. |
VSCBench: Bridging the Gap in Vision-Language Model Safety Calibration (2025.findings-acl)
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Jiahui Geng, Qing Li, Zongxiong Chen, Yuxia Wang, Derui Zhu, Zhuohan Xie, Chenyang Lyu, Xiuying Chen, Preslav Nakov, Fakhri Karray
| Challenge: | Existing safety calibration methods focus on model undersafety, where the model responds to hazardous queries, while neglecting oversafetiness, where models refuse to answer safe queries. |
| Approach: | They propose safety calibration which addresses both undersafety and oversafetiness by comparing model responses to a novel dataset of 3,600 image-text pairs. |
| Outcome: | The proposed methods have been used to evaluate safety calibration across image-centric and text-centric scenarios. |
HD-NDEs: Neural Differential Equations for Hallucination Detection in LLMs (2025.acl-long)
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| Challenge: | Hallucination is a significant challenge for large language models, but current methods struggle when non-factual information arises in the early or mid-sequence of outputs, reducing their reliability. |
| Approach: | They propose a method that captures the full dynamics of large language models by using neural differential equations to assess the truthfulness of statements. |
| Outcome: | The proposed method achieves 14% improvement in AUC-ROC on the True-False dataset compared to state-of-the-art methods. |
Improving Factual Consistency in Abstractive Summarization with Sentence Structure Pruning (2024.lrec-main)
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Dingxin Hu, Xuanyu Zhang, Xingyue Zhang, Yiyang Li, Dongsheng Chen, Marina Litvak, Natalia Vanetik, Qing Yang, Dongliang Xu, Yanquan Zhou, Lei Li, Yuze Li, Yingqi Zhu
| Challenge: | Abstractive summarization models suffer from factual inconsistency problem . post-editing methods focus on replacing suspicious entities, failing to modify incorrect content hidden in sentence structures. |
| Approach: | They propose to use sentence pruning operation to correct possible errors . they propose to apply sentence pruning operations to the syntactic dependency tree . |
| Outcome: | The proposed method improves factual consistency on the FRANK dataset compared with baselines . it is model-independent and can serve as the final step in ensuring factual consistentness. |
Cause-CSD: A Challenge Multimodal Conversational Stance Cause Detection Dataset and Effective Method (2026.findings-acl)
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| Challenge: | Existing stance detection methods treat opinions as surface-level labels, overlooking conversational evidence behind stance expressions. |
| Approach: | They propose a task that jointly identifies stance polarity and contextual evidence . they propose stance-cause Detection language model that leverages explicit context reasoning . |
| Outcome: | The proposed task outperforms baseline methods on text-only and multimodal subtasks. |
Cognitive Policy-Driven LLM for Diagnosis and Intervention of Cognitive Distortions in Emotional Support Conversation (2026.acl-long)
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| Challenge: | Existing models for ESC ignore cognitive distortions in help-seekers' expressions . current models provide basic emotional comfort, rather than helping help- seekers address psychological distress at a deeper cognitive level. |
| Approach: | They propose a Large Language Model framework to enhance LLMs' ability to diagnose and intervene cognitive distortions in help-seekers. |
| Outcome: | The proposed framework outperforms 15 state-of-the-art baselines in terms of distortion diagnosis accuracy, intervention strategy effectiveness, and safety risk control. |
HydraRAG: Structured Cross-Source Enhanced Large Language Model Reasoning (2025.emnlp-main)
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| Challenge: | Current RAG system retrieves evidence from knowledge graphs and text documents but has limitations in multi-hop reasoning, multi-entity questions, and source verification. |
| Approach: | They propose a training-free framework that unifies graph topology, document semantics, and source reliability to support deep, faithful reasoning in large language models. |
| Outcome: | The proposed framework outperforms the current hybrid model-based model-driven system by 20.3% and 30.1% on seven benchmark datasets. |
Reference-free Hallucination Detection for Large Vision-Language Models (2024.findings-emnlp)
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| Challenge: | Large vision-language models exhibit excellent ability in language understanding, question answering, and conversations of visual inputs, but they are prone to producing hallucinations. |
| Approach: | They propose to use supervised uncertainty quantification methods to detect hallucinations in large vision-language models. |
| Outcome: | The proposed methods outperform the others in detecting hallucinations on four representative LVLMs across two different tasks. |
ContrastKV: Robust KV Cache Eviction via Contrastive Signal Fusion for Multi-Query Generalization (2026.acl-long)
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| Challenge: | Existing query-agnostic approaches rely on a single proxy query, leading to fragile eviction decisions under high evict ratios. |
| Approach: | They propose a query-agnostic KV cache eviction algorithm that exploits complementary semantic and non-semantic signals. |
| Outcome: | Experiments show that the proposed algorithm outperforms state-of-the-art methods while retaining up to 92% accuracy with only 20% of the KV cache budget. |
Turning Trash into Treasure: Accelerating Inference of Large Language Models with Token Recycling (2025.acl-long)
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| Challenge: | Large Language Models (LLMs) generate only one token at each decoding step, leading to high latency. |
| Approach: | They propose a speculative decoding paradigm that stores tokens in an adjacency matrix and employs a breadth-first-search algorithm to construct a draft tree. |
| Outcome: | The proposed method outperforms existing train-free methods by 30% and even a training method by 25%. |
SpecCoT: Accelerating Chain-of-Thought Reasoning through Speculative Exploration (2025.findings-emnlp)
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| Challenge: | Large Reasoning Models suffer from high inference latency due to lengthy reasoning chains. |
| Approach: | They propose a collaborative framework that combines large and small models for effective reasoning. |
| Outcome: | The proposed framework reduces inference latency by 1.7-4.1 while maintaining comparable accuracy to standard large model inference. |