Papers by Qian Xiao
Through the MUD: A Multi-Defendant Charge Prediction Benchmark with Linked Crime Elements (2024.acl-long)
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| Challenge: | Existing charge prediction datasets focus on single-defendant cases, but real-world cases involve multiple defendants. |
| Approach: | They propose a benchmark that encompasses legal cases involving multiple defendants . they develop an interpretable model called EJudge that incorporates crime elements and legal rules to infer charges. |
| Outcome: | The proposed model outperforms state-of-the-art models in predicting crime charges while providing corresponding rationales. |
PTCSpell: Pre-trained Corrector Based on Character Shape and Pinyin for Chinese Spelling Correction (2023.findings-acl)
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| Challenge: | Chinese spelling correction (CSC) is a task which detects incorrect characters in Chinese text and corrects them. |
| Approach: | They propose to pre-train a Chinese spelling correction corrector under the detector-corrector architecture and propose to capture pronunciation and shape information in Chinese characters. |
| Outcome: | The proposed corrector achieves an average of 5.8% F1 improvements over state-of-the-art methods, verifying its effectiveness. |
SEGMENT+: Long Text Processing with Short-Context Language Models (2024.emnlp-main)
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Wei Shi, Shuang Li, Kerun Yu, Jinglei Chen, Zujie Liang, Xinhui Wu, Yuxi Qian, Feng Wei, Bo Zheng, Jiaqing Liang, Jiangjie Chen, Yanghua Xiao
| Challenge: | Existing frameworks that increase context window do not guarantee robust performance across long input tasks. |
| Approach: | They propose a framework that enables language models to handle extended inputs within limited context windows efficiently. |
| Outcome: | The framework improves performance on long-document question-answering and Needle-in-a-Haystack tasks. |
Guided Knowledge Generation with Language Models for Commonsense Reasoning (2024.findings-emnlp)
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| Challenge: | Large Language Models (LLMs) have achieved notable success in commonsense reasoning tasks, benefiting from extensive world knowledge acquired through extensive pretraining. |
| Approach: | They propose a method to generate knowledge explanations and to automatically assign labels based on the probability of correct answers. |
| Outcome: | The proposed method outperforms baselines on four widely-used commonsense reasoning benchmarks and shows that it can generate high quality knowledge leading to correct answers. |
GLM: General Language Model Pretraining with Autoregressive Blank Infilling (2022.acl-long)
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| Challenge: | Existing pretraining frameworks do not perform well for all tasks of three main categories, such as natural language understanding (NLU), unconditional generation, and conditional generation. |
| Approach: | They propose a general language model based on autoregressive blank infilling to address this challenge. |
| Outcome: | The proposed model outperforms BERT, T5, and GPT on a wide range of tasks across NLU, conditional and unconditional generation tasks. |
CLEAR: A Framework Enabling Large Language Models to Discern Confusing Legal Paragraphs (2025.findings-emnlp)
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| Challenge: | Existing work focuses on enabling LLMs to leverage legal rules to tackle complex legal reasoning tasks, but ignores their ability to understand legal rules. |
| Approach: | They propose a legal paragraph prediction task that aims to predict the legal paragraph given criminal facts and a framework CLEAR to enhance their legal reasoning ability. |
| Outcome: | The proposed model improves the ability of LLMs to analyze legal cases with the guidance of legal rule insights. |
Global Structure Knowledge-Guided Relation Extraction Method for Visually-Rich Document (2023.findings-emnlp)
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| Challenge: | Existing methods focus on manipulating entity features to find pairwise relations, yet neglect the more fundamental structural information that links disparate entity pairs together. |
| Approach: | They propose a Visual Relation Extraction framework that generates relation predictions on entity pairs extracted from scanned images and incorporates global structural knowledge into the representations of the entities. |
| Outcome: | The proposed framework outperforms existing methods in fine-tuning setting and yields stronger data-efficient performance in the low-resource setting. |
i-Code V2: An Autoregressive Generation Framework over Vision, Language, and Speech Data (2024.findings-naacl)
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Ziyi Yang, Mahmoud Khademi, Yichong Xu, Reid Pryzant, Yuwei Fang, Chenguang Zhu, Dongdong Chen, Yao Qian, Xuemei Gao, Yi-Ling Chen, Robert Gmyr, Naoyuki Kanda, Noel Codella, Bin Xiao, Yu Shi, Lu Yuan, Takuya Yoshioka, Michael Zeng, Xuedong Huang
| Challenge: | i-Code V2 is one of the first models capable of generating natural language from any combination of Vision, Language, and Speech data. |
| Approach: | They propose to create a model that can generate natural language from any combination of Vision, Language, and Speech data. |
| Outcome: | i-Code V2 matches or outperforms state-of-the-art single- and dual-modality baselines on 7 multimodal tasks. |
ReasonMed: A 370K Multi-Agent Generated Dataset for Advancing Medical Reasoning (2025.emnlp-main)
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Yu Sun, Xingyu Qian, Weiwen Xu, Hao Zhang, Chenghao Xiao, Long Li, Deli Zhao, Wenbing Huang, Tingyang Xu, Qifeng Bai, Yu Rong
| Challenge: | Existing medical reasoning datasets are limited in scale and typically rely on incomplete data. |
| Approach: | They propose to use ReasonMed to train medical reasoning models using a multi-agent generation, verification, and refinement pipeline. |
| Outcome: | The largest medical reasoning dataset to date surpasses the prior best sub-10B models by 4.17% and even exceeds LLaMA3.1-70B on PubMedQA by 4.60%. |
KRLS: Improving End-to-End Response Generation in Task Oriented Dialog with Reinforced Keywords Learning (2023.emnlp-main)
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| Challenge: | Task-oriented dialogs (TOD) require a model to generate a response that optimizes for task-related metrics. |
| Approach: | They propose a faster generation procedure that samples from independent next-word distributions and introduce a fine-grained reward function to help the model focus on learning key information in a dialog. |
| Outcome: | The proposed algorithm achieves state-of-the-art performance on an offline task with 15% training time reduction compared to a standard RL algorithm using auto-regressive generation. |
Revisiting Interpolation Augmentation for Speech-to-Text Generation (2024.findings-acl)
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Chen Xu, Jie Wang, Xiaoqian Liu, Qian Dong, Chunliang Zhang, Tong Xiao, JingBo Zhu, Dapeng Man, Wu Yang
| Challenge: | Existing approaches to speech-to-text generation tasks are limited by the lack of extensive labeled datasets. |
| Approach: | They propose to use interpolation augmentation to construct virtual training samples by transforming inputs and labels to enhance generalization in other domains. |
| Outcome: | The proposed approach significantly improves performance across diverse tasks, architectures, and data scales, offering a promising avenue for more robust S2T systems in resource-constrained settings. |
Saber: Efficient Sampling with Adaptive Acceleration and Backtracking Enhanced Remasking for Diffusion Language Model in Code Generation (2026.acl-long)
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| Challenge: | Diffusion language models (DLMs) offer advantages in parallel generation and bidirectional context modeling, but they face a critical trade-off between inference speed and output quality for tasks with strict structural constraints such as code generation. |
| Approach: | They propose an efficient sampling algorithm that reduces the number of tokens unmasked per step based on the model’s evolving confidence. |
| Outcome: | The proposed method improves Pass@1 accuracy by 1.9% while achieving 251.4% inference speedup. |
Divide and Conquer: Legal Concept-guided Criminal Court View Generation (2024.findings-emnlp)
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| Challenge: | Existing methods for creating rationales for criminal cases do not pay enough attention to the important legal concepts. |
| Approach: | They propose a legal concept-guided court view generation framework that generates rationales based on predicted legal concepts . they first divide the court view into sub-views, then employ a solver and verifier to generate and select rationale. |
| Outcome: | The proposed model generates coherent and coherent court views on a real-world criminal case dataset. |
Reinforcing Agentic Search Via Reward Density Optimization (2026.acl-long)
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| Challenge: | Reinforcement Learning with Verifiable Rewards (RLVR) is a promising approach for enhancing agentic search, but its performance is often hindered by reward sparsity . |
| Approach: | They propose a new research problem to improve the reward obtained per unit of exploration cost by using a system that decomposes long-horizon tasks into intermediate objectives and assigns process-level rewards to provide denser learning signals. |
| Outcome: | The proposed framework outperforms strong baselines on several agentic search benchmarks and achieves comparable performance to that of advanced proprietary models. |
Copyright Detective: A Forensic System to Evidence LLMs Flickering Copyright Leakage Risks (2026.acl-demo)
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Guangwei Zhang, Jianing Zhu, Cheng Qian, Neil Zhenqiang Gong, Rada Mihalcea, Zhaozhuo Xu, Jingrui He, Jiaqi W. Ma, Chaowei Xiao, Bo Li, Ahmed Abbasi, Dongwon Lee, Heng Ji, Denghui Zhang
| Challenge: | **Copyright Detective** is the first interactive forensic system for detecting, analyzing, and visualizing potential copyright risks in LLM outputs. |
| Approach: | They propose a system that detects copyright infringements and visualizes them . they use content recall testing, paraphrase-level similarity analysis and persuasive jailbreak probing . |
| Outcome: | The proposed system detects, analyzes, and visualizes potential copyright risks in LLM outputs. |
Capturing Conversational Interaction for Question Answering via Global History Reasoning (2022.findings-naacl)
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| Challenge: | Existing studies have studied history-dependent reasoning for question answering . utilizing global conversation history for enhancement is gaining interest . |
| Approach: | They propose to establish long-distance dependency among global utterances in multi-turn conversation. |
| Outcome: | The proposed method improves on QuAC by 1%, yielding the F1 score of 73.7%. |
APEX: Learning Adaptive Priorities for Multi-Objective Alignment in Vision-Language Generation (2026.findings-acl)
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Dongliang Chen, Xinlin Zhuang, Junjie Xu, Luojian Xie, Zehui Wang, Jiaxi Zhuang, Haolin Yang, Liang Dou, Xiao He, Xingjiao Wu, Ying Qian
| Challenge: | APEX optimizes for text-to-image generation by combining learning potential, conflict penalty, and progress need. |
| Approach: | They propose an algorithm that stabilizes heterogeneous rewards and dynamically schedules objectives . they propose a method that achieves better Pareto trade-offs across four heterogenous objectives based on P3 Adaptive Priorities . |
| Outcome: | The proposed algorithm achieves better pareto trade-offs across four heterogeneous objectives while maintaining competitive OCR accuracy. |
Improving Visual-Semantic Embedding with Adaptive Pooling and Optimization Objective (2023.eacl-main)
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Zijian Zhang, Chang Shu, Ya Xiao, Yuan Shen, Di Zhu, Youxin Chen, Jing Xiao, Jey Han Lau, Qian Zhang, Zheng Lu
| Challenge: | Recent VSE models combine simple pooling methods with hard triplet loss to improve performance. |
| Approach: | They propose an adaptive pooling strategy that allows the model to learn how to aggregate features through a combination of simple pooling methods. |
| Outcome: | The proposed strategy outperforms current state-of-the-art systems on image-to-text and text-toimage retrieval. |