Papers by Miao Xie
SMiLE: Schema-augmented Multi-level Contrastive Learning for Knowledge Graph Link Prediction (2022.findings-emnlp)
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| Challenge: | Existing methods for linking knowledge graphs lack contextual information in entity neighborhoods, which leads to false prediction results. |
| Approach: | They propose a Schema-augmented Multi-level contrastive LEarning framework to conduct knowledge graph link prediction using a knowledge graph schema. |
| Outcome: | The proposed framework is based on a knowledge graph schema and is compared against state-of-the-art datasets. |
Structure Guided Retrieval-Augmented Generation for Factual Queries (2026.acl-long)
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| Challenge: | Existing methods for RAG produce factually incorrect outputs, resulting in incorrect answers. |
| Approach: | They propose a novel problem that explicitly incorporates structural information into RAG for factual questions to satisfy all query conditions. |
| Outcome: | The proposed method significantly outperforms baselines on ERQA while maintaining reasonable computational overhead. |
Reasoning under Uncertainty: Efficient LLM Inference via Unsupervised Confidence Dilution and Convergent Adaptive Sampling (2025.emnlp-main)
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Zhenning Shi, Yijia Zhu, Yi Xie, Junhan Shi, Guorui Xie, Haotian Zhang, Yong Jiang, Congcong Miao, Qing Li
| Challenge: | Large language models suffer from overconfidence and computational inefficiency due to fixed computation budgets and miscalibrated confidence estimates. |
| Approach: | They propose a framework for computationally efficient, trustworthy reasoning under uncertainty using Diversity-Aware Self-Signal Dilution and Convergent Adaptive Weighted Sampling techniques. |
| Outcome: | The proposed framework reduces inference cost by 70% while maintaining accuracy levels while reducing inference costs. |
DeltaScore: Fine-Grained Story Evaluation with Perturbations (2023.findings-emnlp)
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| Challenge: | Existing evaluation metrics for stories are limited in assessing intricate aspects of storytelling, such as fluency and interestingness. |
| Approach: | They propose a novel method that uses perturbation techniques to evaluate story aspects . they compare fluency, coherence, relatedness, logicality, interestingness and interestingness to existing metrics . |
| Outcome: | The proposed method shows that one specific perturbation is highly effective in capturing multiple aspects. |
Audio-Reasoner: Improving Reasoning Capability in Large Audio Language Models (2025.emnlp-main)
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| Challenge: | Recent advances in multimodal reasoning overlook the audio modality. |
| Approach: | They propose a large-scale audio language model for deep reasoning that leverages a multitask audio dataset. |
| Outcome: | The proposed model performs well across key benchmarks including MMAU-mini, AIR-Bench chat/foundation, and MELD. |
Are Large Language Models Chronically Online Surfers? A Dataset for Chinese Internet Meme Explanation (2025.emnlp-main)
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| Challenge: | Large language models (LLMs) are trained on vast amounts of text from the Internet, but do they understand the viral content that rapidly spreads online? |
| Approach: | They introduce a dataset for CHinese Internet Meme Explanation that includes popular phrase-based memes from the Chinese Internet. |
| Outcome: | The proposed dataset includes popular phrase-based memes from the Chinese Internet, annotated with detailed information on their meaning, origin, example sentences, types, etc. |
RD-MCSA: A Multi-Class Sentiment Analysis Approach Integrating In-Context Classification Rationales and Demonstrations (2025.emnlp-main)
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| Challenge: | Existing methods for multi-class sentiment analysis (MCSA) are difficult due to subtle semantic differences between adjacent sentiment levels and the scarcity of high-quality annotated data. |
| Approach: | They propose a framework to integrate classification rationales with adaptively selected demonstrations to enhance MCSA performance under limited supervision. |
| Outcome: | The proposed framework outperforms baseline and standard ICL methods on five benchmark datasets. |