Papers by Xiang Yao
RotateCT: Knowledge Graph Embedding by Rotation and Coordinate Transformation in Complex Space (2022.coling-1)
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| Challenge: | Existing knowledge graph embedding methods fail to model non-commutative composition patterns . Existing methods are limited to complex space, resulting in a large number of parameters. |
| Approach: | They propose a knowledge graph embedding method that transforms the coordinates of each entity and then represents each relation as a rotation from head entity to tail entity in complex space. |
| Outcome: | The proposed method outperforms state-of-the-art methods on link prediction and path query answering. |
SGA-MCTS: Decoupling Planning from Execution via Training-Free Atomic Experience Retrieval (2026.findings-acl)
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| Challenge: | a new framework casts LLM planning as non-parametric retrieval, but high latency of inference-time search and supervised fine-tuning are limitations. |
| Approach: | They propose a framework that casts LLM planning as non-parametric retrieval . they leverage Monte Carlo Tree Search to explore the solution space . |
| Outcome: | Empirical results show that SGA-MCTS can match the performance of SOTA systems without task-specific fine-tuning. |
VerilogLAVD: LLM-Aided Pattern Generation for Verilog CWE Detection (2026.acl-long)
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| Challenge: | Existing static analysis tools focus on functional correctness and depend heavily on manual rules. |
| Approach: | They propose a framework that generates executable Traversal Detection Patterns (TDPs) to help detect hardware vulnerabilities. |
| Outcome: | The proposed framework improves the F1 score by 133% compared to LLM-based methods. |
Good Visual Guidance Make A Better Extractor: Hierarchical Visual Prefix for Multimodal Entity and Relation Extraction (2022.findings-naacl)
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Xiang Chen, Ningyu Zhang, Lei Li, Yunzhi Yao, Shumin Deng, Chuanqi Tan, Fei Huang, Luo Si, Huajun Chen
| Challenge: | Existing approaches for named entity recognition and relation extraction suffer from error sensitivity when irrelevant object images are incorporated in texts. |
| Approach: | They propose a hierarchical visual prefix fusion NeTwork for visual-enhanced entity and relation extraction using pluggable visual prefixed visual features. |
| Outcome: | The proposed method achieves state-of-the-art on three benchmark datasets. |
C-MORE: Pretraining to Answer Open-Domain Questions by Consulting Millions of References (2022.acl-short)
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| Challenge: | Existing approaches to pretrain open-domain question answering systems lack task-specific annotations. |
| Approach: | They propose to pretrain a two-stage open-domain question answering system with strong transfer capabilities by using a dictionary and a large-scale corpus. |
| Outcome: | The proposed approach leads to 2%-10% gains in top-20 accuracy and improves with reader. |
CRAB: Cross-environment Agent Benchmark for Multimodal Language Model Agents (2025.findings-acl)
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Tianqi Xu, Linyao Chen, Dai-Jie Wu, Yanjun Chen, Zecheng Zhang, Xiang Yao, Zhiqiang Xie, Yongchao Chen, Shilong Liu, Bochen Qian, Anjie Yang, Zhaoxuan Jin, Jianbo Deng, Philip Torr, Bernard Ghanem, Guohao Li
| Challenge: | Existing benchmarks for MLM agents in interactive environments are limited by their focus on a single environment, lack of detailed and generalized evaluation methods, and the complexity of constructing tasks and evaluators. |
| Approach: | They propose a cross-environment agent benchmark framework that integrates graph-based evaluation and task generation methods. |
| Outcome: | The proposed framework supports multiple devices and can be easily extended to any environment with a Python interface. |
Reasoning with Language Model Prompting: A Survey (2023.acl-long)
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Shuofei Qiao, Yixin Ou, Ningyu Zhang, Xiang Chen, Yunzhi Yao, Shumin Deng, Chuanqi Tan, Fei Huang, Huajun Chen
| Challenge: | Reasoning is an essential ability for complex problem-solving and can provide back-end support for various real-world applications. |
| Approach: | They present cutting-edge research on reasoning with language model prompting and provide systematic resources to help beginners. |
| Outcome: | The proposed approaches have not been systematically reviewed and analyzed. |
Enhancing LLM-based Hatred and Toxicity Detection with Meta-Toxic Knowledge Graph (2025.findings-acl)
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| Challenge: | Existing methods to address toxicity issues with large language models are inadequate . lack of domain-specific knowledge leads to false negatives and excessive sensitivity to toxic speech limits freedom of speech. |
| Approach: | They propose a method that leverages graph search on a meta-toxic knowledge graph to enhance hatred and toxicity detection. |
| Outcome: | The proposed method lowers false positive rate and improves toxicity detection performance in out-of-domain scenarios. |
AutoPRM: Automating Procedural Supervision for Multi-Step Reasoning via Controllable Question Decomposition (2024.naacl-long)
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| Challenge: | Recent advances in large language models (LLMs) have shown promise in multi-step reasoning tasks, yet relying on extensive manual labeling to provide procedural feedback remains a significant impediment. |
| Approach: | They propose a self-supervised framework that decomposes complex problems into manageable subquestions with a controllable granularity switch and sequentially applies reinforcement learning to iteratively improve the subquest solver. |
| Outcome: | The proposed framework improves performance on mathematical and commonsense reasoning tasks over SOTA. |
ASD-iLLM:An Intervention Large Language Model for Autistic Children based on Real Clinical Dialogue Intervention Dataset (2025.findings-emnlp)
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Shuzhong Lai, Chenxi Li, Junhong Lai, Yucun Zhong, Chenyu Yan, Xiang Li, Haifeng Li, Gang Pan, Lin Yao, Yueming Wang
| Challenge: | Currently, leveraging large language models (LLMs) for autism intervention is a significant yet challenging task, especially when directly employing LLMs as an intervention doctor. |
| Approach: | They propose a framework for training LLMs to conduct dialogue interventions in accordance with the principles of Applied Behavior Analysis (ABA) they also propose 'role-play' strategy in which LLM act as autistic children to comprehensively evaluate the doctor model's capabilities at the dialogue level. |
| Outcome: | The proposed framework outperforms existing models in both automatic and human evaluation, with intervention strategies and dialogue style more closely resembling those of clinical intervention doctors. |
Machine Unlearning of Pre-trained Large Language Models (2024.acl-long)
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| Challenge: | Using curated datasets, we establish a robust benchmark for unlearning performance, demonstrating that these methods are over 105 times more computationally efficient than retraining. |
| Approach: | They propose a framework for machine unlearning in pre-trained LLMs and integrate gradient ascent with gradient descent on in-distribution data to achieve robustness. |
| Outcome: | The proposed framework is over 105 times more efficient than retraining on in-distribution data and provides detailed guidelines for efficient hyperparameter tuning in the unlearning process. |
MoDE-CoTD: Chain-of-Thought Distillation for Complex Reasoning Tasks with Mixture of Decoupled LoRA-Experts (2024.lrec-main)
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| Challenge: | Current Chain-of-thought Distillation methods hinder CoT reasoning performance . student models are separately distilled from specific reasoning tasks . parameter update of student models severely harms CoT ability on unseen reasoning tasks. |
| Approach: | They propose a method which distills Chain-of-thought reasoning ability of large language models to much smaller student models. |
| Outcome: | The proposed method improves the reasoning ability of large language models on 14 datasets. |
SGCD: Subtask-Guided Causal-Debiasing Framework for Robust Cross-Utterance Sentiment Quadruple Extraction in Dialogues (2025.findings-emnlp)
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| Challenge: | a new framework for sentiment analysis in dialogues addresses cross-utterance elements and focus biases . SGCD framework employs multi-granularity attention paths to enhance cross-interaction matching . |
| Approach: | a framework is developed to help analyze sentiments in multi-turn dialogues . it leverages subtask-specific features to guide learning of token-level features . |
| Outcome: | The proposed framework outperforms state-of-the-art methods in analyzing conversational data . cross-utterance elements and focus bias are challenges, authors say . |
CoMMIT: Coordinated Multimodal Instruction Tuning (2025.emnlp-main)
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Xintong Li, Junda Wu, Tong Yu, Rui Wang, Yu Wang, Xiang Chen, Jiuxiang Gu, Lina Yao, Julian McAuley, Jingbo Shang
| Challenge: | et al., 2024) show that multimodal instruction tuning is more effective than baselines. |
| Approach: | They propose a multimodal balance coefficient that enables quantitative measurement of the balance of learning . they propose auxiliary regularization on the gradient to promote updating with larger step sizes . |
| Outcome: | The proposed method is more effective than baselines in MLLM instruction tuning. |
Takin-VC: Expressive Zero-Shot Voice Conversion via Adaptive Hybrid Content Encoding and Enhanced Timbre Modeling (2025.acl-long)
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Yang Yuguang, Yu Pan, Jixun Yao, Xiang Zhang, Jianhao Ye, Hongbin Zhou, Lei Xie, Lei Ma, Jianjun Zhao
| Challenge: | Expressive zero-shot voice conversion (VC) aims to modify source timbre to match unseen speaker . existing zero- shot VC systems struggle to reproduce paralinguistic information in highly expressive speech . |
| Approach: | They propose a framework for expressive zero-shot voice conversion that uses hybrid content encoding and memory-augmented context-aware timbre modeling. |
| Outcome: | The proposed framework surpasses state-of-the-art VC systems in speech naturalness, speaker similarity, and speaker similarness. |
Synthetic Question Value Estimation for Domain Adaptation of Question Answering (2022.acl-long)
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| Challenge: | Existing work adapts QA scores to select high-quality questions, but these scores do not improve QA performance on the target domain. |
| Approach: | They propose to synthesize QA pairs with a question generator on the target domain . they propose to train a Question Value Estimator that estimates usefulness of synthetic questions . |
| Outcome: | The proposed method improves the performance of the target domain QA model by using synthetic questions and only 15% of the human annotations on the targetdomain. |
Knowledge Mechanisms in Large Language Models: A Survey and Perspective (2024.findings-emnlp)
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Mengru Wang, Yunzhi Yao, Ziwen Xu, Shuofei Qiao, Shumin Deng, Peng Wang, Xiang Chen, Jia-Chen Gu, Yong Jiang, Pengjun Xie, Fei Huang, Huajun Chen, Ningyu Zhang
| Challenge: | Using large language models, we can understand knowledge mechanisms in LLMs for learning, storage, utilization, and evolution. |
| Approach: | They propose to analyze knowledge mechanisms in Large Language Models (LLMs) they examine utilization, evolution, and the potential dark knowledge (hypothesis) they hope to help understand knowledge in LLMs and provide insights for future research . |
| Outcome: | The proposed model can be used to analyze the evolution of parametric knowledge in LLMs. |
RIMRULE: Improving Tool-Using Language Agents via MDL-Guided Rule Learning (2026.acl-long)
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Xiang Gao, Yuguang Yao, Qi Zhang, Kaiwen Dong, Avinash Baidya, Ruocheng Guo, Hilaf Hasson, Kamalika Das
| Challenge: | Large language models (LLMs) struggle to use tools reliably in domain-specific settings. |
| Approach: | They propose a neuro-symbolic approach to adapt large language models to task-specific tools . they propose reusable rules that are distilled from failure traces and injected into the prompt . |
| Outcome: | Experiments show that the proposed approach outperforms prompting-based adaptation methods and complements finetuning. |
Can Large Language Models Act as Ensembler for Multi-GNNs? (2025.emnlp-main)
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| Challenge: | Existing graph neural networks lack the inherent semantic understanding capability of rich textual attributes, limiting their effectiveness in applications. |
| Approach: | They propose a model that integrates multiple GNNs and LLMs to provide an ensemble for multi-GNNs. |
| Outcome: | The proposed model outperforms existing models in terms of semantic understanding of graph structures and graph structures. |