Papers by Xiaofei Xu
Contrastive Document Representation Learning with Graph Attention Networks (2021.findings-emnlp)
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| Challenge: | Existing methods for document representation learning are significantly affected by the scarcity of document-level data. |
| Approach: | They propose to use a graph attention network on top of the available pretrained Transformers model to learn document embeddings. |
| Outcome: | Empirically, the proposed approach is effective in document classification and document retrieval tasks. |
Teaching Large Language Models Number-Focused Headline Generation With Key Element Rationales (2025.findings-naacl)
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| Challenge: | Existing studies focus only on textual quality and numerical accuracy for headline generation. |
| Approach: | They propose a framework for using rationales of key elements of Topic, Entities, and Numerical reasoning in news articles to enhance LLMs' ability to generate topic-aligned texts with precise numerical accuracy. |
| Outcome: | The proposed framework improves the ability of large language models to generate high-quality texts with precise numerical accuracy. |
Tree of Agents: Improving Long-Context Capabilities of Large Language Models through Multi-Perspective Reasoning (2025.findings-emnlp)
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| Challenge: | Large language models face persistent challenges when handling long-context tasks . existing methods that reduce input have the risk of discarding key information . |
| Approach: | To address this issue, we propose a multi-agent reasoning framework called Tree of Agents . the framework segments input into chunks processed by independent agents . |
| Outcome: | The proposed model outperforms baseline models on long-context tasks. |
Entailment Tree Explanations via Iterative Retrieval-Generation Reasoner (2022.findings-naacl)
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Danilo Neves Ribeiro, Shen Wang, Xiaofei Ma, Rui Dong, Xiaokai Wei, Henghui Zhu, Xinchi Chen, Peng Xu, Zhiheng Huang, Andrew Arnold, Dan Roth
| Challenge: | Large language models have achieved high performance on various natural language benchmarks, but the explainability of their output remains elusive. |
| Approach: | They propose an architecture called iterative retrieval-generation reasoner that generates an entailment tree that explains a given hypothesis by using premises from C. |
| Outcome: | The proposed model outperforms existing benchmarks on premise retrieval and entailment tree generation with around 300% gain in overall correctness. |
Where Did It Go Wrong? Capability-Oriented Failure Attribution for Vision-and-Language Navigation Agents (2026.findings-acl)
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| Challenge: | Existing testing methods are system-level and provide limited insight into which capability deficiencies cause task failures. |
| Approach: | They propose a capability-oriented testing approach that enables failure detection and attribution by seed selection and mutation. |
| Outcome: | The proposed method detects more failure cases and pinpoints capability-level deficiencies than state-of-the-art baselines, providing more interpretable and actionable guidance for improving embodied agents. |
Domain Adaptation with BERT-based Domain Classification and Data Selection (D19-61)
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| Challenge: | Modern deep neural models with millions of parameters can easily adapt to a new learning task and dataset when enough supervision is given. |
| Approach: | They propose a domain adaptation framework based on curriculum learning and domain-discriminative data selection. |
| Outcome: | The proposed framework outperforms discrepancy-based methods on transfer tasks while consuming only fraction of training budget. |
Enhancing Cross-target Stance Detection with Transferable Semantic-Emotion Knowledge (2020.acl-main)
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| Challenge: | Existing methods for stance detection are struggling to cope with the data across targets. |
| Approach: | They propose a model that uses external knowledge as a bridge to enable knowledge transfer across different targets. |
| Outcome: | The proposed model outperforms existing methods on a large real-world dataset. |