Papers by Xiangkun Hu
An AMR-based Link Prediction Approach for Document-level Event Argument Extraction (2023.acl-long)
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| Challenge: | Recent work has introduced Abstract Meaning Representation (AMR) for Document-level Event Argument Extraction (Doc-level EAE) however, in these works AMR is used only implicitly, for instance, as additional features or training signals. |
| Approach: | They propose a novel AMR-based graph structure which uses graph neural networks to find event arguments from unstructured text. |
| Outcome: | The proposed graph structure outperforms the state-of-the-art models by 3.63pt and 2.33pt F1 and reduces inference time by 56%. |
Synergetic Event Understanding: A Collaborative Approach to Cross-Document Event Coreference Resolution with Large Language Models (2024.acl-long)
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| Challenge: | Existing approaches to cross-document event coreference resolution are prone to learning simple co-occurrences due to the complexity of contexts. |
| Approach: | They propose a collaborative approach to cross-document event coreference resolution that leverages both a universally capable LLM and a task-specific SLM. |
| Outcome: | The proposed approach surpasses the performance of both large and small language models individually, underscoring its effectiveness in diverse scenarios. |
RLET: A Reinforcement Learning Based Approach for Explainable QA with Entailment Trees (2022.emnlp-main)
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| Challenge: | Existing structured reasoning frameworks lack internal decision probability and cannot model the tree as a whole. |
| Approach: | They propose a Reinforcement Learning based Entailment Tree generation framework that is trained using the cumulative signals across the whole tree. |
| Outcome: | The proposed framework offers explicit deductions with entailment steps in a tree structure. |
Exploiting Abstract Meaning Representation for Open-Domain Question Answering (2023.findings-acl)
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| Challenge: | Existing work attempts to address these challenges using Pretrained Language Models (PLMs) but the diversity of surface form expressions can hinder the model’s ability to capture accurate correlations, especially when the context is lengthy and complex. |
| Approach: | They propose a method known as Graph-as-Token (GST) to incorporate AMRs into PLMs to assist the model in understanding complex semantic information. |
| Outcome: | The proposed method outperforms existing methods and significantly improves performance on both Natural Questions and TriviaQA. |
DeepResearcher: Scaling Deep Research via Reinforcement Learning in Real-world Environments (2025.emnlp-main)
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| Challenge: | Large Language Models (LLMs) with web search capabilities show significant potential for deep research. |
| Approach: | They introduce a framework for end-to-end training of LLM-based deep research agents . they implement a specialized multi-agent architecture where browsing agents extract relevant information from various webpage structures. |
| Outcome: | The proposed framework improves on open-domain research tasks by 28.9 points over prompt engineering and 7.2 points over RAG-based RL agents. |
Knowledge-Centric Hallucination Detection (2024.emnlp-main)
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Xiangkun Hu, Dongyu Ru, Lin Qiu, Qipeng Guo, Tianhang Zhang, Yang Xu, Yun Luo, Pengfei Liu, Yue Zhang, Zheng Zhang
| Challenge: | Large Language Models (LLMs) have shown impressive capabilities but a tendency to hallucinate. |
| Approach: | They propose a framework that introduces claim-triplets to represent claims in LLM responses and evaluates them against a reference. |
| Outcome: | The proposed framework outperforms prior methods by 18.2 to 27.2 points on a benchmark spanning various NLP tasks and annotated 11k claim-triplets from 2.1k responses by seven LLMs. |
Dual Cache for Long Document Neural Coreference Resolution (2023.acl-long)
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| Challenge: | Recent studies show the effectiveness of cache-based neural coreference resolution models on long documents. |
| Approach: | They propose a hybrid cache that integrates two eviction policies to capture global and local entities separately and improves F1 score of coreference by 0.7 5.7pt. |
| Outcome: | The proposed model outperforms existing models on four benchmarks while saving up to 83% of inference time. |
Dialogue Meaning Representation for Task-Oriented Dialogue Systems (2022.findings-emnlp)
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| Challenge: | Existing work on dialogue meaning representations is limited in scalability for complex expressions. |
| Approach: | They propose a pliable and easily extendable representation for task-oriented dialogue . they propose an inheritance hierarchy mechanism focusing on domain extensibility . |
| Outcome: | The proposed representation can be easily extended to a task-oriented dialogue dataset. |
Incorporating Argument-Level Interactions for Persuasion Comments Evaluation using Co-attention Model (C18-1)
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| Challenge: | Existing research explores different text features of reply comments on word level and ignores interactions between participants. |
| Approach: | They propose a co-attention mechanism based neural network to capture interactions between participants on argument level to better model dialogical argumentation. |
| Outcome: | The proposed model outperforms state-of-the-art methods on a publicly available dataset showing that it extracts interactive argument pairs from the original post and the reply. |
Plan, Verify and Switch: Integrated Reasoning with Diverse X-of-Thoughts (2023.emnlp-main)
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| Challenge: | Existing methods for large language models (LLMs) have been used to prompt different reasoning thoughts, such as Chain of Thought and Program of Though. |
| Approach: | They propose a framework that prompts large language models with diverse reasoning thoughts by iterating between different prompting methods. |
| Outcome: | The proposed framework is able to generate multiple reasoning thoughts in 10 popular math reasoning datasets and is orthogonal to recent work that makes improvements on single reasoning methods and can generalise to logical reasoning domain. |
Multi-Document Event Extraction Using Large and Small Language Models (2025.emnlp-main)
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| Challenge: | Existing approaches to multi-document event extraction have limited attention . despite its practical significance, this task has inherent challenges . |
| Approach: | They propose a collaborative framework that integrates large language models for multi-step reasoning and fine-tuned small language models to handle key subtasks. |
| Outcome: | The proposed framework outperforms existing methods and provides new insights into collaborative reasoning to tackle the complexities of multi-document event extraction. |
PerSphere: A Comprehensive Framework for Multi-Faceted Perspective Retrieval and Summarization (2025.acl-long)
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| Challenge: | Experimental results show that the main challenge lies in long context and perspective extraction. |
| Approach: | They propose a benchmark to facilitate multi-faceted perspective retrieval and summarization . they propose measurable metrics to evaluate the comprehensiveness of the retrieval pipeline . |
| Outcome: | The proposed system breaks free from information silos by combining two opposing claims . it can be used to extract multiple perspectives and improve performance on the platform . |