Papers by Hongyuan Yuan
DTDES-KGE: Dual-Teacher Knowledge Distillation with Distinct Embedding Spaces for Knowledge Graph Embeddings (2025.findings-emnlp)
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| Challenge: | Existing knowledge distillation methods rely on a single teacher embedding space . existing methods overlook valuable complementary knowledge from teachers in distinct embeddable spaces. |
| Approach: | They propose a knowledge distillation framework that leverages dual teachers in embedding spaces to enhance performance. |
| Outcome: | The proposed framework significantly improves knowledge distillation performance by leveraging dual teachers in distinct embedding spaces. |
TacoPrompt: A Collaborative Multi-Task Prompt Learning Method for Self-Supervised Taxonomy Completion (2023.emnlp-main)
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| Challenge: | Existing methods for automating taxonomy completion use subtasks to learn subtask results, ignoring the effects of subtask on the final prediction. |
| Approach: | They propose a multi-task automatic taxonomy completion method that attaches emerging concepts to an appropriate pair of hypernym and hyponym in existing taxonomies. |
| Outcome: | The proposed method improves on three datasets and improves inference efficiency. |
TEMP: Taxonomy Expansion with Dynamic Margin Loss through Taxonomy-Paths (2021.emnlp-main)
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| Challenge: | Existing taxonomies are unable to maintain coverage due to the rising of new concepts . TEMP uses pre-trained contextual encoders to predict the position of new ideas . |
| Approach: | They propose a self-supervised taxonomy expansion method that ranks taxonomies by ranking them . they use pre-trained contextual encoders to train the model with dynamic margin loss . |
| Outcome: | The proposed method outperforms state-of-the-art taxonomy expansion methods by 14.3% and 15.8% on public benchmarks. |
Bridging the Sensory Gap: Visual Injection for Taxonomy Completion (2026.acl-long)
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| Challenge: | Existing text-only methods suffer from a "Sensory Gap" in integrating new concepts into existing hierarchies. |
| Approach: | They propose a framework leveraging Visual Injection for Taxonomy Completion that maps synthesized images into intrinsic pseudo-tokens and decouples magnitude from selection to prevent visual signals from being drowned out. |
| Outcome: | Experiments on three datasets show that VITC achieves state-of-the-art performance . it delivers an average absolute gain of over 19% in Hit@1. |
Graph-Based Chain-of-Thought Pruning for Reducing Redundant Reflections in Reasoning LLMs (2026.findings-acl)
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Hongyuan Yuan, Xinran He, Run Shao, Bolei He, Xianwei Xue, Mengke Chen, Qiutong Pan, Haiwei Wang, Haifeng Li
| Challenge: | Extending CoT through RL can induce undesirable thinking patterns such as overthinking . prior work has focused on inefficient reflection, which manifests in two problematic patterns: Indiscriminate Reflection and Repetitive Reflectione . |
| Approach: | They propose a graph-based approach to optimize CoT by pruning each linear CoT into a directed acyclic graph with explicit dependency edges. |
| Outcome: | The proposed approach reduces the average reasoning tokens by 42% while maintaining or improving accuracy. |