Papers by Bowei Yang
Distributed LLM Serving on Consumer-Grade GPUs by Reconciling Computation and Communication (2025.findings-emnlp)
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Lewei Jin, Kui Zhang, Yongqi Chen, null Zhuoyifan, Renjie Li, Yi Gao, Bowei Yang, Zhengong Cai, Wei Dong
| Challenge: | Large language models are reshaping internet services, and serving them is costly. |
| Approach: | They propose an efficient distributed LLM serving system that splits prefill and decode requests into smaller chunks . |
| Outcome: | The proposed system reduces TTFT, TPOT, and latency compared to the state-of-the-art system. |
Knowledge Graph Embedding by Adaptive Limit Scoring Loss Using Dynamic Weighting Strategy (2022.findings-acl)
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| Challenge: | Existing knowledge graph embedding models use a loss framework to distinguish between correct and incorrect triplets. |
| Approach: | They propose a loss framework that reweights each triplet to highlight the less-optimized triplets. |
| Outcome: | The proposed method performs on several knowledge graph embedding models, including TransE, TransH and ComplEx. |
Incorporating Image Matching Into Knowledge Acquisition for Event-Oriented Relation Recognition (C18-1)
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| Challenge: | Event relation recognition is a challenging language processing task because the query events are selected from different paragraphs in a document or even different documents, so there is lack of explicit clue. |
| Approach: | They propose to use image processing to acquire similar event instances and use image matching to approximate calculation between events. |
| Outcome: | The proposed model performs comparable to CNN while slightly better than LSTM on the ACE-R2 corpus. |
Learning Hierarchy-Aware Quaternion Knowledge Graph Embeddings with Representing Relations as 3D Rotations (2022.coling-1)
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| Challenge: | Existing knowledge graph embedding models fail to model semantic hierarchies . Existing methods fail to understand the semantic hierarchies of knowledge graphs . |
| Approach: | They propose a model which embeds entities as pure quaternions and constrains the modulus of entities to make them have hierarchical distributions. |
| Outcome: | The proposed model can encode symmetry/antisymmetry, inversion, composition, multiple relation patterns and learn semantic hierarchies simultaneously. |