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Chang Jin

2 accepted papers

2026

When Silence Is Golden: Can LLMs Learn to Abstain in Temporal QA and Beyond?

ICLR 2026poster

Large language models (LLMs) rarely admit uncertainty, often producing fluent but misleading answers, rather than abstaining (i.e., refusing to answer). This weakness is even evident in temporal question answering (QA), where models frequently ignore time-sensitive evidence and conflate facts across…

Cited by 0SourcecodeScholar
2022

AdMix: A Mixed Sample Data Augmentation Method for Neural Machine Translation

IJCAI 2022poster

In Neural Machine Translation (NMT), data augmentation methods such as back-translation have proven their effectiveness in improving translation performance. In this paper, we propose a novel data augmentation approach for NMT, which is independent of any additional training data. Our approach, AdMi…

Cited by 8SourcePDFScholar