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Bei Wu

2 accepted papers

2025

PALA: Class-imbalanced Graph Domain Adaptation via Prototype-anchored Learning and Alignment

IJCAI 2025

Graph domain adaptation is a key subfield of graph transfer learning that aims to bridge domain gaps by transferring knowledge from a label-rich source graph to an unlabeled target graph. However, most existing methods assume balanced labels in the source graph, which often fails in practice and lea

2025

TEaR: Improving LLM-based Machine Translation with Systematic Self-Refinement

NAACL 2025findings

Large Language Models (LLMs) have achieved impressive results in Machine Translation (MT). However, human evaluations reveal that LLM-generated translations still contain various errors. Notably, feeding the error information back into the LLMs can facilitate self-refinement, leading to enhanced tra…