Papers by Xiangliang Zhang
MTSQL-R1: Towards Long-Horizon Multi-Turn Text-to-SQL via Agentic Training (2026.acl-long)
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Taicheng Guo, Hai Wang, Chaochun Liu, Mohsen Golalikhani, Xin Chen, Xiangliang Zhang, Chandan K. Reddy
| Challenge: | Existing systems for multi-turn Text-to-SQL are limited to a short-horizon paradigm, generating a query per turn without execution, explicit verification, and refinement, which leads to non-executable or incoherent outputs. |
| Approach: | They propose to train an agentic training framework for long-horizon multi-turn Text-to-SQL that uses a Markov Decision Process to generate a query per turn without execution, explicit verification, and refinement. |
| Outcome: | Experiments on CoSQL and SParC show that MTSQL-R1 consistently outperforms strong baselines, highlighting the importance of environment-driven verification and memory-guided refinement for conversational semantic parsing. |
ArMATH: a Dataset for Solving Arabic Math Word Problems (2022.lrec-1)
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| Challenge: | This paper is the first to use deep learning methods to solve Arabic MWPs . it is also the first study to use transfer learning to solve MWp across different languages . |
| Approach: | They contribute to the first large-scale dataset for Arabic Math Word Problems . they use deep learning methods to solve Arabic MWPs and a transfer learning model to promote performance . |
| Outcome: | The proposed model improves Arabic MWP solvers by 3% over the existing model. |
Capturing Relations between Scientific Papers: An Abstractive Model for Related Work Section Generation (2021.acl-long)
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| Challenge: | Existing related work generation models are inflexible and extract sentences from multiple papers to form a related work discussion. |
| Approach: | They propose a Relation-aware Related work generator which generates an abstractive related work from the given multiple scientific papers in the same research area. |
| Outcome: | The proposed model improves over existing models and can be used to familiarize researchers with the state of the art in the field. |
1+1>2: Can Large Language Models Serve as Cross-Lingual Knowledge Aggregators? (2024.emnlp-main)
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| Challenge: | Large Language Models (LLMs) have been recognized for their impressive capabilities in natural language processing (NLP). |
| Approach: | They propose a method to enhance the multilingual performance of Large Language Models by aggregating knowledge from diverse languages. |
| Outcome: | The proposed method reduces the performance disparity across languages and offers valuable insights for further exploration. |
UniMath: A Foundational and Multimodal Mathematical Reasoner (2023.emnlp-main)
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| Challenge: | Existing methods for interpreting and processing diverse mathematical modalities are limited . existing systems are limited in interpreting complex mathematical tasks and implementing them in a multimodal manner. |
| Approach: | They propose a multimodal mathematical reasoning system that utilizes a fine-tuned T5 model augmented with a variational autoencoder (VAE)-based image tokenizer. |
| Outcome: | The proposed model achieves state-of-the-art performance on SVAMP, GeoQA, and TableMWP datasets and is generalized on two additional datasets. |
SceMQA: A Scientific College Entrance Level Multimodal Question Answering Benchmark (2024.acl-short)
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Zhenwen Liang, Kehan Guo, Gang Liu, Taicheng Guo, Yujun Zhou, Tianyu Yang, Jiajun Jiao, Renjie Pi, Jipeng Zhang, Xiangliang Zhang
| Challenge: | SceMQA focuses on core science subjects including Mathematics, Physics, Chemistry, and Biology. |
| Approach: | They propose to use SceMQA to evaluate multimodal question answering at college entrance level. |
| Outcome: | The proposed model provides specific knowledge points for each problem and detailed explanations for each answer. |
Scientific Paper Extractive Summarization Enhanced by Citation Graphs (2022.emnlp-main)
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| Challenge: | citation graphs can be used to extract scientific papers under different conditions. |
| Approach: | They propose a multi-granularity unsupervised summarization model that fine tunes a pre-trained encoder model on the citation graph by link prediction tasks. |
| Outcome: | The proposed model outperforms baseline models on a public benchmark dataset. |
MWP-BERT: Numeracy-Augmented Pre-training for Math Word Problem Solving (2022.findings-naacl)
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| Challenge: | Existing work on math word problem solvers replace real numbers with symbolic placeholders to focus on logic reasoning. |
| Approach: | They propose to inject numerical properties into symbolic placeholders with contextualized representation learning schema to solve number representation dilemma. |
| Outcome: | The proposed model can solve MWP problems on English and Chinese benchmarks. |
Too Correct to Learn: Reinforcement Learning on Saturated Reasoning Data (2026.acl-short)
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| Challenge: | Strong base models saturate benchmarks, resulting in weaker performance, a paradox . a new approach to Reinforcement Learning (RL) is needed to improve performance . |
| Approach: | They propose a method that uses constrained uniform top-k sampling to flatten the local optimization landscape by sampling uniformly from constrained high-confidence candidates. |
| Outcome: | Experiments show that the proposed approach prevents policy degeneration and boosts out-of-domain generalization. |
Invisible Entropy: Towards Safe and Efficient Low-Entropy LLM Watermarking (2025.emnlp-main)
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| Challenge: | Existing methods to watermark low-entropy content are expensive and risky . IE reduces parameter size by 99% while achieving performance on par with state-of-the-art methods . |
| Approach: | They propose a logit-based watermarking paradigm that uses entropy-based features to predict whether the next token is high or low. |
| Outcome: | The proposed method reduces parameter size by 99% while achieving performance on par with state-of-the-art methods. |
Beyond Single-Value Metrics: Evaluating and Enhancing LLM Unlearning with Cognitive Diagnosis (2025.findings-acl)
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Yicheng Lang, Kehan Guo, Yue Huang, Yujun Zhou, Haomin Zhuang, Tianyu Yang, Yao Su, Xiangliang Zhang
| Challenge: | Large Language Models (LLMs) have been used to remove harmful knowledge and undesirable capabilities. |
| Approach: | They propose a framework that leverages Cognitive Diagnosis Modeling to evaluate LLM unlearning. |
| Outcome: | The proposed framework enhances evaluation and facilitates removal of harmful abilities. |
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. |
Let GPT be a Math Tutor: Teaching Math Word Problem Solvers with Customized Exercise Generation (2023.emnlp-main)
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| Challenge: | Existing approaches for distilling large language models into smaller, more efficient student models are based on educational science principles such as knowledge tracing and personalized learning. |
| Approach: | They propose a method for distilling large language models into smaller, more efficient student models that are aligned with educational science principles such as knowledge tracing and personalized learning. |
| Outcome: | The proposed approach outperforms LLMs on three benchmarks while employing significantly fewer parameters. |
Data-Efficient Language Shaped Few-shot Image Classification (2021.findings-emnlp)
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| Challenge: | Existing studies have shown that language is helpful guider for image understanding by neural networks. |
| Approach: | They propose a language-shaped learning method that makes the best use of the few-shot images and the language available only in training. |
| Outcome: | The proposed method outperforms state-of-the-art methods on a few-shot dataset with limited training data. |
SEUF: Is Unlearning One Expert Enough for Mixture-of-Experts LLMs? (2025.acl-long)
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| Challenge: | Recent advances in LLMs unlearning have shown remarkable success in removing unwanted data-model influences while preserving the model’s utility for legitimate knowledge. |
| Approach: | They propose a Selected-Expert Unlearning Framework (SEUF) that combines expert attribution and an anchor loss to ensure controlled unlearning. |
| Outcome: | Experiments show that the proposed framework improves forget quality and model utility by 35% on MoE LLMs across benchmarks and LLM architectures compared to standard unlearning algorithms . |
Few-shot Low-resource Knowledge Graph Completion with Reinforced Task Generation (2023.findings-acl)
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| Challenge: | Existing few-shot learning-based models have difficulty alleviating the long-tail issue on low-resource KGs because of the lack of training tasks. |
| Approach: | They propose a few-shot low-resource knowledge graph completion framework that generates and selects beneficial few- shot tasks that complement current tasks. |
| Outcome: | The proposed framework is based on several real-world knowledge graphs and validates on multiple domains. |
Compositional Mathematical Encoding for Math Word Problems (2023.findings-acl)
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| Challenge: | Existing MWP encoders work in a unimodal setting and map problem description to latent representation, then for decoding. |
| Approach: | They propose a Compositional Math Word Problem Solver which maps problem description to latent representation and decodes it in an interactive way. |
| Outcome: | Extensive experiments show that the proposed model outperforms state-of-the-art models on public benchmarks. |
RAt: Injecting Implicit Bias for Text-To-Image Prompt Refinement Models (2024.emnlp-main)
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| Challenge: | Text-to-image prompt refinement (T2I-Refine) aims to rephrase or extend an input prompt with more descriptive details that can be leveraged to generate images with higher quality. |
| Approach: | They develop an adversarial prompt attacking framework that implicitly attacks input prompts with intentional adversarials to generate images with higher quality. |
| Outcome: | The proposed framework can implicitly attack input prompts with implicit concept biases to generate images with higher quality and explicit visual bias towards the target group. |
PolicyLLM: Towards Excellent Comprehension of Public Policy for Large Language Models (2026.findings-acl)
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Han Bao, Penghao Zhang, Yue Huang, Zhengqing Yuan, Yanchi Ru, SU Rui, Yujun Zhou, Xiangqi Wang, Kehan Guo, Nitesh V Chawla, Yanfang Ye, Xiangliang Zhang
| Challenge: | Large Language Models (LLMs) are increasingly integrated into real-world decision-making, but their ability to comprehend and reason about policy-related content remains underexplored. |
| Approach: | They propose a bilingual benchmark evaluating policy comprehension comprising 21K cases across a broad spectrum of policy areas. |
| Outcome: | The proposed model shows stronger performance on application-oriented policy tasks than on memorization or conceptual understanding, and yields the highest accuracy on structured reasoning tasks. |
Analogical Math Word Problems Solving with Enhanced Problem-Solution Association (2022.emnlp-main)
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| Challenge: | Analogical reasoning has long been used in mathematical education, as it enables students to apply common relational structures of mathematical situations to solve new problems. |
| Approach: | They propose to leverage analogical MWPs to advance the solver’s generalization ability across different kinds of MWps. |
| Outcome: | The proposed model has a stronger generalization ability in solving difficult MWPs due to the analogical learning from easy MWPS. |
Quest2DataAgent: Automating End-to-End Scientific Data Collection (2025.emnlp-demos)
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Tianyu Yang, Yuhan Liu, Sobin Alosious, Ethan A. Brown, Jason R. Rohr, Tengfei Luo, Xiangliang Zhang
| Challenge: | Existing approaches for data collection are labor-intensive and dependent on domain expertise. |
| Approach: | They propose a general-purpose multi-agent framework for automating scientific data collection workflows. |
| Outcome: | The proposed framework improves data relevance, usability, and time efficiency over existing methods. |
RiskLab: A Controlled Toolkit for Probing Emergent Risks in LLM-Based Multi-Agent Systems (2026.acl-demo)
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Yu Jiang, Wenjie Wang, Yue Huang, Yanbo Wang, Zhenhong Zhou, Xiuying Chen, Yang Liu, Pin-Yu Chen, Wei Wang, Xiangliang Zhang
| Challenge: | Recent advances in large language model (LLM) agents have accelerated deployment of multi-agent systems for complex tasks. |
| Approach: | They propose an open-source toolkit for instantiating, probing, and measuring emergent risks in LLM-based multi-agent systems under controlled conditions. |
| Outcome: | The proposed toolkit is based on a structured topology–environment–protocol–agent–task quintuple enabling reproducible studies of how communication structure, coordination mechanisms, and incentives shape system-level risks. |
Improving the Robustness of Summarization Systems with Dual Augmentation (2023.acl-long)
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| Challenge: | Experimental results show that state-of-the-art summarization models have a significant decrease in performance on adversarial and noisy test sets. |
| Approach: | They propose a SummAttacker approach to generate adversarial samples based on pre-trained language models that can generate word-level synonym substitution and noise. |
| Outcome: | The proposed model performs better on noisy, attacked, and clean datasets than baseline models and is more robust on noisy and attacked datasets. |
MinT: Boosting Generalization in Mathematical Reasoning via Multi-view Fine-tuning (2024.lrec-main)
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| Challenge: | Existing methods focus on specializing LMs in mathematical reasoning and rely on knowledge distillation. |
| Approach: | They propose a multi-view fine-tuning method that exploits existing mathematical problem datasets with diverse annotation styles. |
| Outcome: | The proposed method outperforms existing methods that rely heavily on LLM teachers . it grants models generalization ability across views and datasets, and the capability to learn from inaccurate or incomplete data. |
Cross-Lingual Pitfalls: Automatic Probing Cross-Lingual Weakness of Multilingual Large Language Models (2025.acl-long)
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| Challenge: | Large language models have achieved remarkable success in Natural Language Processing, yet their cross-lingual consistency remains a significant challenge. |
| Approach: | They propose a method to identify cross-lingual weaknesses in Large Language Models . they construct bilingual question pairs that expose performance discrepancies between English and target languages . |
| Outcome: | The proposed method uncovers over 50% accuracy drops in target languages across models. |
Dissecting Logical Reasoning in LLMs: A Fine-Grained Evaluation and Supervision Study (2025.findings-emnlp)
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Yujun Zhou, Jiayi Ye, Zipeng Ling, Yufei Han, Yue Huang, Haomin Zhuang, Zhenwen Liang, Kehan Guo, Taicheng Guo, Xiangqi Wang, Xiangliang Zhang
| Challenge: | Existing benchmarks that rely on final-answer accuracy fail to capture the quality of the reasoning process. |
| Approach: | They propose a fine-grained evaluation framework that assesses logical reasoning across three dimensions: overall accuracy, stepwise soundness, and representation-level probing. |
| Outcome: | The proposed framework assesses logical reasoning across three dimensions: overall accuracy, stepwise soundness, and representation-level probing. |
A Survey of Multimodal Mathematical Reasoning: From Perception, Alignment to Reasoning (2026.acl-long)
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Tianyu Yang, Sihong Wu, Yilun Zhao, Zhenwen Liang, Lisen Dai, Chen Zhao, Minhao Cheng, Arman Cohan, Xiangliang Zhang
| Challenge: | Multimodal mathematical Reasoning (MMR) has attracted increasing attention for its ability to solve mathematical problems involving both textual and visual modalities. |
| Approach: | They review the theoretical frameworks of multimodal reasoning and examine the challenges they face in visual math tasks. |
| Outcome: | The proposed models can solve problems involving both textual and visual modalities. |
Shaping the Safety Boundaries: Understanding and Defending Against Jailbreaks in Large Language Models (2025.acl-long)
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| Challenge: | Understanding how jailbreaking works remains limited, hindering the development of effective defense strategies. |
| Approach: | They propose a new mechanism that adaptively constrains activations within the safety boundary and propose 'Activation Boundary Defense' to enhance its effectiveness. |
| Outcome: | The proposed defense achieves an average Defense Success Rate (DSR) of over 98% against various jailbreak attacks, with less than 2% impact on the model’s general capabilities. |
CLIPErase: Efficient Unlearning of Visual-Textual Associations in CLIP (2025.acl-long)
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| Challenge: | MU has gained significant attention as a means to remove the influence of specific data from a trained model without requiring full retraining. |
| Approach: | They propose a novel approach that disentangles and selectively forgets both visual and textual associations, ensuring that unlearning does not compromise model performance. |
| Outcome: | Experiments on CIFAR-100, Flickr30K, and Conceptual 12M show that CLIPErase effectively removes designated associations from multimodal samples in downstream tasks while preserving model performance on retain set. |
SaSR-Net: Source-Aware Semantic Representation Network for Enhancing Audio-Visual Question Answering (2024.findings-emnlp)
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| Challenge: | Existing AVQA methods often fail to link sound-producing objects in the video with the audio-visual information. |
| Approach: | They introduce a source-aware semantic representation network for AVQA . they use source-wise learnable tokens to capture and align audio-visual elements with the question . |
| Outcome: | The proposed model outperforms state-of-the-art models on the Music-AVQA and AVQA-Yang datasets. |
Defending Jailbreak Prompts via In-Context Adversarial Game (2024.emnlp-main)
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| Challenge: | Large Language Models (LLMs) demonstrate remarkable capabilities across diverse applications, but concerns regarding their security persist. |
| Approach: | They propose an adversarial game that leverages agent learning to extend knowledge to defend against jailbreaks. |
| Outcome: | The proposed game shows that LLMs safeguarded by ICAG exhibit significantly reduced jailbreak success rates across various attack scenarios. |
ArtELingo: A Million Emotion Annotations of WikiArt with Emphasis on Diversity over Language and Culture (2022.emnlp-main)
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Youssef Mohamed, Mohamed Abdelfattah, Shyma Alhuwaider, Feifan Li, Xiangliang Zhang, Kenneth Church, Mohamed Elhoseiny
| Challenge: | ArtELingo is a benchmark and dataset designed to encourage work on diversity across languages and cultures. |
| Approach: | They introduce a benchmark and dataset designed to encourage work on diversity across languages and cultures. |
| Outcome: | The new benchmark and dataset compared artELingo annotations across languages and cultures and found that diversity improves the performance of baseline models. |