Papers by Xiaoye Qu
HacRED: A Large-Scale Relation Extraction Dataset Toward Hard Cases in Practical Applications (2021.findings-acl)
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Qiao Cheng, Juntao Liu, Xiaoye Qu, Jin Zhao, Jiaqing Liang, Zhefeng Wang, Baoxing Huai, Nicholas Jing Yuan, Yanghua Xiao
| Challenge: | Relation extraction (RE) is an essential topic in natural language processing and has attracted extensive attention. |
| Approach: | They propose a case-oriented construction framework to build a hard case relation extraction dataset with 65,225 relational facts annotated from 9,231 documents. |
| Outcome: | The proposed model achieves a high 96% F1 score on data quality and is far lower than humans. |
Cooperative or Competitive? Understanding the Interaction between Attention Heads From A Game Theory Perspective (2025.acl-long)
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| Challenge: | a number of attention-based large language models (LLMs) focus on individual head contributions, but the precise interaction mechanisms between attention heads remain poorly understood. |
| Approach: | They propose a game-theoretic attention calibration method that uses the Harsanyi dividend . they selectively retain heads demonstrating significant cooperative gains and apply fine-grained adjustments to remaining heads . |
| Outcome: | The proposed framework is based on the Harsanyi dividend, a concept from cooperative game theory. |
Delving Deep into Regularity: A Simple but Effective Method for Chinese Named Entity Recognition (2022.findings-naacl)
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| Challenge: | Named entity recognition (NER) is a system for identifying text spans pertaining to specific entity types. |
| Approach: | They propose a method to investigate the regularity of Chinese NER's entity mentions by a regularity-aware module and a periodicity-gnostic module. |
| Outcome: | The proposed model significantly outperforms previous state-of-the-art methods on three benchmark datasets and a practical medical dataset. |
Mitigating Boundary Ambiguity and Inherent Bias for Text Classification in the Era of Large Language Models (2024.findings-acl)
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| Challenge: | a new text classification framework for large language models addresses the problem of boundary ambiguity and inherent biases in LLMs. |
| Approach: | They propose a two-stage classification framework for large language models to mitigate bottlenecks . their approach uses pairwise comparisons to efficiently narrow down options . |
| Outcome: | The proposed framework reduces the number of options and improves on four datasets. |
SEE: Continual Fine-tuning with Sequential Ensemble of Experts (2025.findings-acl)
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| Challenge: | Continual fine-tuning of large language models suffers from catastrophic forgetting . some approaches use routers to assign tasks to experts, but continual learning often requires retraining . |
| Approach: | They propose a framework that integrates routing and response mechanisms within each expert . it eliminates the need for an additional router and allows each expert to decide whether a query should be handled . |
| Outcome: | The proposed framework outperforms previous approaches in continual fine-tuning . it can handle learning tasks and out-of-distribution instances, paving the way for distributed model ensembling. |
Multi-level Association Refinement Network for Dialogue Aspect-based Sentiment Quadruple Analysis (2025.acl-long)
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| Challenge: | Existing methods for identifying quadruples rely on predefined dialogue structure and word semantics to achieve accurate and comprehensive sentiment associations between utterances and words. |
| Approach: | They propose a multi-level association refinement network to achieve more accurate sentiment associations between utterances and words. |
| Outcome: | The proposed framework achieves state-of-the-art performance under low-resource conditions. |
Adversarial Category Alignment Network for Cross-domain Sentiment Classification (N19-1)
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| Challenge: | Existing methods for cross-domain sentiment classification focus on aligning marginal distribution without taking category-specific decision boundaries into consideration. |
| Approach: | They propose an adversarial category alignment network to enhance category consistency . experimental results show the proposed method can achieve state-of-the-art performance . |
| Outcome: | The proposed method achieves state-of-the-art performance and produces more discriminative features on benchmark datasets. |
Living in the Moment: Can Large Language Models Grasp Co-Temporal Reasoning? (2024.acl-long)
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| Challenge: | Current temporal reasoning datasets are limited to questions about single or isolated events, falling short in mirroring the realistic temporal characteristics involving concurrent nature and intricate temporal interconnections. |
| Approach: | They propose a co-temporal Question Answering benchmark that contains four co-time scenarios with 4,748 samples for evaluating the co-timing abilities of large language models. |
| Outcome: | The proposed benchmarks show that current LLMs struggle on CoTempQA tasks even when enhanced with Chain of Thought methodologies. |
Mirror: A Universal Framework for Various Information Extraction Tasks (2023.emnlp-main)
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Tong Zhu, Junfei Ren, Zijian Yu, Mengsong Wu, Guoliang Zhang, Xiaoye Qu, Wenliang Chen, Zhefeng Wang, Baoxing Huai, Min Zhang
| Challenge: | Recent studies often formulate IE tasks as a triplet extraction problem, but this paradigm does not support multi-span and n-ary extraction, leading to weak versatility. |
| Approach: | They propose a multi-span cyclic graph extraction problem and a non-autoregressive graph decoding algorithm to extract all spans in a single step. |
| Outcome: | The proposed model outperforms or reaches competitive performance with SOTA systems under few-shot and zero-shot settings and it is compatible with 57 datasets. |
Progressively Guide to Attend: An Iterative Alignment Framework for Temporal Sentence Grounding (2021.emnlp-main)
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| Challenge: | Existing methods to learn effective alignment between vision and language features are insufficient in practice due to complicated multi-step reasoning. |
| Approach: | They propose an iterative alignment network which iterates inter- and intra-modal features within multiple steps for more accurate grounding. |
| Outcome: | The proposed model performs better than the state-of-the-arts on three challenging benchmarks. |
Look, Compare, Decide: Alleviating Hallucination in Large Vision-Language Models via Multi-View Multi-Path Reasoning (2025.coling-main)
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| Challenge: | Large Vision-Language Models (LVLMs) have impressive capabilities in multi-modal context comprehension, but they still suffer from hallucination problems due to inconsistent outputs with the image content. |
| Approach: | They propose a training-free framework MVP to reduce hallucinations in Large Vision-Language Models . they propose multi-view information-seeking strategy to perceive the comprehensive information in the image . |
| Outcome: | The proposed framework reduces hallucinations in large vision-language models by combining multi-view multi-path reasoning with multi-vision multi-path reasoning. |
Miracle: Towards Personalized Dialogue Generation with Latent-Space Multiple Personal Attribute Control (2023.findings-emnlp)
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| Challenge: | Personalized dialogue systems aim to endow the chatbot agent with more anthropomorphic traits for human-like interactions. |
| Approach: | They propose a method to generate personalized dialogues using latent-space energy-based models by using a latent space energy-model. |
| Outcome: | The proposed method outperforms baselines in personality controllability and response quality. |
PRMBench: A Fine-grained and Challenging Benchmark for Process-Level Reward Models (2025.acl-long)
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| Challenge: | Recent large language models (LLMs) have achieved significant performance in complex reasoning tasks such as mathematics and code generation. |
| Approach: | They propose a process-level benchmark specifically designed to assess the fine-grained error detection capabilities of PRMs. |
| Outcome: | The proposed model measures the accuracy, soundness, and sensitivity of 25 models across open-source and closed-source large language models. |
Confidence is not Timeless: Modeling Temporal Validity for Rule-based Temporal Knowledge Graph Forecasting (2024.acl-long)
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| Challenge: | Existing literature on temporal knowledge Graph Forecasting lacks in-depth investigation into how confidence evolves with time. |
| Approach: | They propose a framework to model the temporal validity of rules for Temporal Knowledge Graph Forecasting (TKGF) they propose rule-adversarial negative sampling and time-aware negative sampling strategies to facilitate TempValid learning. |
| Outcome: | The proposed framework outperforms state-of-the-art (SOTA) rule-based methods on six TKGF datasets. |
TREA: Tree-Structure Reasoning Schema for Conversational Recommendation (2023.acl-long)
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| Challenge: | Recent reasoning-based models cannot fully figure out complex causal relationships between mentioned entities with external knowledge. |
| Approach: | They propose a Tree structure Reasoning schEmA that constructs a multi-hierarchical scalable tree as the reasoning structure to clarify the causal relationships between mentioned entities. |
| Outcome: | Extensive experiments on two public CRS datasets show the proposed model works. |
Towards Robust Temporal Activity Localization Learning with Noisy Labels (2024.lrec-main)
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Daizong Liu, Xiaoye Qu, Xiang Fang, Jianfeng Dong, Pan Zhou, Guoshun Nan, Keke Tang, Wanlong Fang, Yu Cheng
| Challenge: | Existing methods for temporal activity localization are expensive and difficult to satisfy due to subjective labeling. |
| Approach: | They propose a new TAL setting where a TAL model should be robust to mixed training data with noisy moment boundaries. |
| Outcome: | The proposed method is significantly more robust to noisy training data than existing methods. |
Dynamic Data Mixing Maximizes Instruction Tuning for Mixture-of-Experts (2025.naacl-long)
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| Challenge: | Mixture-of-Experts (MoE) models are constrained by their fixed model capacities when the number of tasks grows in instruction tuning. |
| Approach: | They propose to combine all training tasks and apply fixed sampling weights without considering the importance of different tasks as the model training state changes. |
| Outcome: | The proposed method can be used on knowledge & reasoning tasks and open-ended queries with limited training budget. |
Scaling Reasoning, Losing Control: Evaluating Instruction Following in Large Reasoning Models (2026.acl-long)
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| Challenge: | Recent advances in reasoning-oriented models have demonstrated impressive capabilities in mathematical reasoning, but their ability to adhere to user directives remains underexplored. |
| Approach: | They propose a benchmark to evaluate instruction-following in mathematical reasoning tasks. |
| Outcome: | The proposed model degrades in instruction adherence when generation length increases, but can partially recover obedience, despite increasing generation length. |
SURf: Teaching Large Vision-Language Models to Selectively Utilize Retrieved Information (2024.emnlp-main)
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| Challenge: | Existing studies focus on the text modality or are limited to specific tasks. |
| Approach: | They propose a framework to teach Large Vision-Language Models to selectively utilize retrieved information and improve their robustness against irrelevant or misleading references. |
| Outcome: | The proposed framework improves LVLMs’ ability to utilize retrieved multimodal references and their robustness against irrelevant or misleading information. |
LLaMA-MoE: Building Mixture-of-Experts from LLaMA with Continual Pre-Training (2024.emnlp-main)
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| Challenge: | Mixture-of-Experts (MoE) has gained increasing popularity as a framework for scaling up large language models. |
| Approach: | They investigate how to build Mixture-of-Experts (MoE) models from existing large language models . they use expert construction, Continual pre-training and data sampling strategies . |
| Outcome: | The proposed model outperforms existing models with similar parameters on a wide range of tasks. |
Adaptive Proposal Generation Network for Temporal Sentence Localization in Videos (2021.emnlp-main)
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| Challenge: | Temporal sentence localization in videos is an important yet challenging task in natural language processing. |
| Approach: | They propose an Adaptive Proposal Generation Network to maintain the segment-level interaction while speeding up the efficiency. |
| Outcome: | The proposed model outperforms state-of-the-art methods on three challenging benchmarks. |
Reasoning Step-by-Step: Temporal Sentence Localization in Videos via Deep Rectification-Modulation Network (2020.coling-main)
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| Challenge: | Existing methods for temporal sentence localization in videos focus on visual content, but they are insufficient to model complex video contents. |
| Approach: | They propose a deep rectification-modulation network to correct attention misalignment . they use sentence information to capture frame-to-frame relation . |
| Outcome: | The proposed method achieves state-of-the-art performance on three public datasets. |
CLIP-MoE: Towards Building Mixture of Experts for CLIP with Diversified Multiplet Upcycling (2025.emnlp-main)
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| Challenge: | Recent studies found that CLIP can only encode one aspect of the feature space, leading to substantial information loss and indistinctive features. |
| Approach: | They propose a model-agnostic approach that fine-tunes complementary CLIP models and transforms them into a CLIP-MoE. |
| Outcome: | The proposed framework fine-tunes a series of complementary CLIP models and transforms them into a CLIP-MoE. |