EMA cuts adaptation costs in learning-based systems by 14.9-42.4% and raises performance by 6.9-31.3% via state transformers for input alignment and prioritized high-utility data labeling.
Matthew Honnibal, Ines Montani, Sofie Van Lan- deghem, and Adriane Boyd
2 Pith papers cite this work. Polarity classification is still indexing.
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2026 2verdicts
UNVERDICTED 2representative citing papers
UnIte selects target-domain documents for pseudo-query generation by filtering high aleatoric uncertainty and prioritizing high epistemic uncertainty, yielding +2.45 to +3.49 nDCG@10 gains on BEIR with ~4k samples.
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EMA: Efficient Model Adaptation for Learning-based Systems
EMA cuts adaptation costs in learning-based systems by 14.9-42.4% and raises performance by 6.9-31.3% via state transformers for input alignment and prioritized high-utility data labeling.
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UnIte: Uncertainty-based Iterative Document Sampling for Domain Adaptation in Information Retrieval
UnIte selects target-domain documents for pseudo-query generation by filtering high aleatoric uncertainty and prioritizing high epistemic uncertainty, yielding +2.45 to +3.49 nDCG@10 gains on BEIR with ~4k samples.