Papers by Boyang Wang
Improving Factual Consistency for Knowledge-Grounded Dialogue Systems via Knowledge Enhancement and Alignment (2023.findings-emnlp)
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Boyang Xue, Weichao Wang, Hongru Wang, Fei Mi, Rui Wang, Yasheng Wang, Lifeng Shang, Xin Jiang, Qun Liu, Kam-Fai Wong
| Challenge: | Experimental results show that pretrained language models generate inconsistent factual knowledge in many conversational tasks. |
| Approach: | They propose a method which explicitly introduces extended feedforward networks (FFNs) in Transformers to enhance factual knowledge expressions given the specific patterns of knowledge-grounded dialogue inputs. |
| Outcome: | The proposed methods improve the factual expression capability of feedforward networks (FFNs) in knowledge-grounded dialogue systems by knowledge enhancement and alignment respectively. |
AppBench: Planning of Multiple APIs from Various APPs for Complex User Instruction (2024.emnlp-main)
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| Challenge: | Existing state-of-the-art Large Language Models (LLMs) still cannot perform well in this situation even with the help of in-context learning and finetuning. |
| Approach: | They propose a benchmark to evaluate LLMs’ ability to plan and execute multiple APIs from various sources in order to complete the user’s task. |
| Outcome: | The proposed benchmarks show that the existing state-of-the-art LLMs still cannot perform well in this situation even with in-context learning and finetuning. |
SPHERE: Unveiling Spatial Blind Spots in Vision-Language Models Through Hierarchical Evaluation (2025.acl-long)
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Wenyu Zhang, Wei En Ng, Lixin Ma, Yuwen Wang, Junqi Zhao, Allison Koenecke, Boyang Li, Wanglu Wanglu
| Challenge: | Current vision-language models lack multi-dimensional spatial reasoning capabilities for human-like understanding and applications. |
| Approach: | They propose a hierarchical evaluation framework that probes models across increasing levels of complexity and integrates spatial, visual, and logical understanding. |
| Outcome: | The proposed framework probes models across increasing levels of complexity, from basic skills to multi-skill integration and high-level reasoning that combines spatial, visual, and logical understanding. |
Improving Knowledge Graph Embedding Using Simple Constraints (P18-1)
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| Challenge: | Recent efforts focused on designing more complicated models or incorporating extra information beyond triples. |
| Approach: | They propose to use non-negativity constraints on entity representations and approximate entailment constraints on relation representations to improve KG embedding. |
| Outcome: | The proposed model outperforms baseline models on WordNet, Freebase, and DBpedia. |
Self-Error-Instruct: Generalizing from Errors for LLMs Mathematical Reasoning (2025.acl-long)
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Erxin Yu, Jing Li, Ming Liao, Qi Zhu, Boyang Xue, Minghui Xu, Baojun Wang, Lanqing Hong, Fei Mi, Lifeng Shang
| Challenge: | Existing approaches to learning from errors synthesize training data by extrapolating from isolated bad cases, thereby failing to generalize the extensive patterns inherent within these cases. |
| Approach: | They propose a framework that synthesizes more generalized training data from isolated bad cases by extrapolating from isolated cases. |
| Outcome: | The proposed framework synthesizes more generalized training data to address these model weaknesses. |
MlingConf: A Comprehensive Study of Multilingual Confidence Estimation on Large Language Models (2025.findings-acl)
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Boyang Xue, Hongru Wang, Rui Wang, Sheng Wang, Zezhong Wang, Yiming Du, Bin Liang, Wenxuan Zhang, Kam-Fai Wong
| Challenge: | Existing studies on LLM confidence estimations in languages other than English have been limited to English. |
| Approach: | They propose to use question-related language to prompt LLMs to assess their confidence in large language models. |
| Outcome: | The proposed model improves on question-related language prompts for LS tasks, while English exhibits notable linguistic dominance in confidence estimations. |
MasRouter: Learning to Route LLMs for Multi-Agent Systems (2025.acl-long)
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| Challenge: | Multi-agent systems (MAS) powered by Large Language Models (LLMs) have been demonstrated to push the boundaries of LLM capabilities, yet they often face significant costs and challenges in dynamic LLM selection. |
| Approach: | They propose a multi-agent system routing solution that integrates all components of MAS into a unified routing framework. |
| Outcome: | The proposed solution is high-performing, cost-effective, and efficient . it reduces overhead by up to 52.07 compared to current methods on HumanEval . |
AgentGym: Evaluating and Training Large Language Model-based Agents across Diverse Environments (2025.acl-long)
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Zhiheng Xi, Yiwen Ding, Wenxiang Chen, Boyang Hong, Honglin Guo, Junzhe Wang, Xin Guo, Dingwen Yang, Chenyang Liao, Wei He, Songyang Gao, Lu Chen, Rui Zheng, Yicheng Zou, Tao Gui, Qi Zhang, Xipeng Qiu, Xuanjing Huang, Zuxuan Wu, Yu-Gang Jiang
| Challenge: | Large language models (LLMs) are promising foundations to build generally-capable agents . however, the community lacks a unified interactive framework that covers diverse environments for comprehensive evaluation of agents. |
| Approach: | They propose a framework that features 7 real-world scenarios, 14 environments, and 89 tasks for unified, real-time, and concurrent agent interaction. |
| Outcome: | The proposed framework features 7 real-world scenarios, 14 environments, and 89 tasks for unified, real-time, and concurrent agent interaction. |
Mitigating Context Interference for Reliable and Efficient Search Agents (2026.acl-long)
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Boyang Xue, Bin Wu, Shuofei Qiao, Sheng Wang, Rui Wang, Yiming Du, Hongru Wang, Jeff Z. Pan, Emine Yilmaz, Kam-Fai Wong, Aldo Lipani
| Challenge: | Recent research empowers Large Language Models (LLMs) as multi-turn search agents to iteratively retrieve and generate outputs until complex tasks are solved. |
| Approach: | They propose a distill-based context refiner to dynamically mitigate context interference . they also propose RLs that refine contexts to generate outputs . |
| Outcome: | The proposed refiner can mitigate context interference in multi-turn search agents. |
M2C: Towards Automatic Multimodal Manga Complement (2023.findings-emnlp)
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| Challenge: | Multimodal manga analysis focuses on enhancing manga understanding with visual and textual features. |
| Approach: | They propose a task to enhance manga understanding with visual and textual features by providing a shared semantic space for vision and language understanding. |
| Outcome: | The proposed task provides a shared semantic space for vision and language understanding. |
History-Aware Hierarchical Transformer for Multi-session Open-domain Dialogue System (2022.findings-emnlp)
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| Challenge: | Existing open-domain dialogue systems conduct one-session conversations, but multi-session MSCs are under-investigated. |
| Approach: | They propose a History-Aware Hierarchical Transformer for multi-session open-domain dialogue . they propose to encode history conversations into a history memory and leverage historical information to generate well-informed responses. |
| Outcome: | The proposed model outperforms baseline models on a large-scale MSC dataset. |
WebAggregator: Enhancing Compositional Reasoning Capabilities of Deep Research Agent Foundation Models (2026.acl-long)
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Rui Wang, Ce Zhang, Jun-Yu Ma, Jianshu Zhang, Hongru Wang, Yi Chen, Boyang Xue, Tianqing Fang, Zhisong Zhang, Hongming Zhang, Haitao Mi, Dong Yu, Kam-Fai Wong
| Challenge: | Existing agentic systems are retrieval-heavy but reasoning-light . current systems lack compositional reasoning, a key component of deep research . |
| Approach: | They propose a data synthesis pipeline WebAggregator to shift agentic paradigm . they use Proactive Explorer to collect interconnected knowledge and Compositional Logic Proposer to weave knowledge into complex questions . |
| Outcome: | The proposed pipeline surpasses GPT-4.1 and matches Claude-3.7-Sonnet on GAIA, WebWalkerQA, and XBench. |
LoRA Meets Dropout under a Unified Framework (2024.findings-acl)
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| Challenge: | Parameter-efficientfinetuning (PEFT) has gained popularity as a lightweight approach for model customization. |
| Approach: | They propose a parameter-efficient dropout method that is overfitting-prone and parameter-freezed. |
| Outcome: | The proposed method is superior to existing methods and compares with transformer-specific methods. |
Annotating High-Level Structures of Short Stories and Personal Anecdotes (L18-1)
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| Challenge: | Existing theories for narrative structures have been challenging to operationalize . authors present an annotation scheme to help computer systems understand stories better . |
| Approach: | They propose to consolidate and extend existing narratological theories and an annotation scheme . they will support an approach that enables systems to intelligently sustain complex communications with humans . |
| Outcome: | The proposed method consolidates and extends existing narratological theories . it will support an approach that enables systems to intelligently sustain complex communications with humans . |
Enhancing Large Language Models Against Inductive Instructions with Dual-critique Prompting (2024.naacl-long)
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| Challenge: | Existing studies have focused on how LLMs handle inductive instructions, which may stem from users’ false beliefs or malicious intents. |
| Approach: | They propose a benchmark of Inductive Instructions where false knowledge is incorporated into instructions in multiple different styles. |
| Outcome: | The proposed model improves robustness against inductive instructions, despite different inductive styles and complexity. |
UniRetriever: Multi-task Candidates Selection for Various Context-Adaptive Conversational Retrieval (2024.lrec-main)
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| Challenge: | Existing methods for retrieving information from a large corpus of data are sub-optimal and low efficiency. |
| Approach: | They propose a multi-task framework that functions as a universal retriever for three dominant retrieval tasks during the conversation. |
| Outcome: | The proposed framework can perform persona selection, knowledge selection, and response selection tasks simultaneously. |
Self-DC: When to Reason and When to Act? Self Divide-and-Conquer for Compositional Unknown Questions (2025.naacl-long)
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Hongru Wang, Boyang Xue, Baohang Zhou, Tianhua Zhang, Cunxiang Wang, Huimin Wang, Guanhua Chen, Kam-Fai Wong
| Challenge: | Existing studies focus on leveraging internal knowledge of Large Language Models (LLMs) to answer known questions. |
| Approach: | They propose a framework that allows LLMs to choose between internal and external knowledge . they use a dataset to analyze compositional questions that are composed of unknown sub-questions . |
| Outcome: | The proposed framework can achieve comparable or even better performance with much fewer external calls compared with several strong baselines. |
LCDS: A Logic-Controlled Discharge Summary Generation System Supporting Source Attribution and Expert Review (2025.acl-demo)
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| Challenge: | Large language models (LLMs) are capable of generating inaccurate discharge summary content or fabricating information without valid sources. |
| Approach: | They propose a tool for empowering LLMs with Logic-Controlled Discharge Summary generation. |
| Outcome: | The proposed tool identifies the writing logic of discharge summaries and integrates it with EMRs to generate silver discharge summararies. |
Where CoT Reasoning Commits: Entropy Traces Identify Interpretable Attention Heads (2026.findings-acl)
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| Challenge: | a growing body of work suggests a disconnect between the generated rationale and the model's actual choice. |
| Approach: | They propose a mechanism-aware framework that interprets the evolving "choice state" of attention heads during CoT generation . they identify a set of intervention targets and perform Selective Head Fine-Tuning . |
| Outcome: | The proposed framework interprets the "choice state" of attention heads during CoT generation . it detects two functional behaviors: Steadfast Heads and Wavering Heads . |
RedOne: Revealing Domain-specific LLM Post-Training in Social Networking Services (2025.emnlp-industry)
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Fei Zhao, Chonggang Lu, null Wangyue, Zheyong Xie, Ziyan Liu, Haofu Qian, Jianzhao Huang, Fangcheng Shi, Zijie Meng, Hongcheng Guo, Mingqian He, Xinze Lyu, Zheyu Ye, Weiting Liu, Boyang Wang, Shaosheng Cao
| Challenge: | Social networking services (SNS) have experienced rapid growth, which has proposed significant challenges for platform content management and interaction quality improvement. |
| Approach: | They propose a domain-specific LLM to break the performance bottleneck of single-task baselines and establish a comprehensive foundation for social networking services. |
| Outcome: | The proposed model achieves an average improvement of 14.02% across 8 major tasks and 7.56% in bilingual evaluation benchmark, compared with baseline models. |
Towards Multi-System Log Anomaly Detection (2025.acl-industry)
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| Challenge: | Existing models require dataset-specific training, causing costly procedures and performance bottlenecks. |
| Approach: | They propose a log anomaly detection model with semantic relational reasoning that extracts cross-system semantic patterns and encodes them as high-dimensional learnable vectors. |
| Outcome: | The proposed model extracts cross-system semantic patterns and encodes them as high-dimensional learnable vectors. |
UAlign: Leveraging Uncertainty Estimations for Factuality Alignment on Large Language Models (2025.acl-long)
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| Challenge: | Large Language Models (LLMs) often struggle to accurately express factual knowledge, especially in cases where the knowledge boundaries are ambiguous. |
| Approach: | They propose a framework that leverages Uncertainty estimations to represent knowledge boundaries and incorporates these representations into prompts for LLMs to Align with factual knowledge. |
| Outcome: | The proposed framework significantly improves the LLMs’ capacities to confidently answer known questions and refuse unknown questions on both in-domain and out-of-domain tasks. |
Self-Sum: Teaching an Agent to Decide Itself When and What to Summarize (2026.findings-acl)
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Hongru Wang, Rui Wang, Jushi Kai, Boyang Xue, Yongqi Li, Shijue Huang, Xiaoteng Ma, Jeff Z. Pan, Amos Storkey
| Challenge: | Existing methods for summarizing long-horizon agents rely on fixed, rule-based summarization strategies. |
| Approach: | They propose a framework that empowers agents to autonomously decide when and what to summarize by modeling it as an internal cognitive action unified with environmental actions. |
| Outcome: | The proposed framework outperforms no-summarization and rule-based training methods on long-horizon benchmarks and shows strong generalization gains. |
Role Prompting Guided Domain Adaptation with General Capability Preserve for Large Language Models (2024.findings-naacl)
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| Challenge: | Large Language Models (LLMs) suffer catastrophic forgetting when tailored to specific domains . authors present a novel approach to manage multi-domain LLM adaptation . |
| Approach: | They propose a strategy to manage multi-domain LLM adaptation using self-distillation and role integration. |
| Outcome: | The proposed model alleviates catastrophic forgetting and inter-domain confusion while maintaining robust general capabilities. |
PRoLoRA: Partial Rotation Empowers More Parameter-Efficient LoRA (2024.acl-long)
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| Challenge: | Partially Rotation-enhanced Low-Rank Adaptation (PRoLoRA) is an intra-layer sharing mechanism that circumvents the drawbacks of peer parameter-sharing methods. |
| Approach: | They propose a partially rotation-enhanced low-rank adaptation (PRoLoRA) that shares four components to reduce the cost of LoRA and improves model capacity. |
| Outcome: | Empirical results show that PRoLoRA outperforms LoRA on multiple instruction tuning datasets. |