Papers with static prefiltering

1 papers
ReSURE: Regularizing Supervision Unreliability for Multi-turn Dialogue Fine-tuning (2025.emnlp-main)

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Challenge: Existing methods address data quality via static prefiltering, which decouples quality control from training and fails to mitigate turn-level error propagation.
Approach: They propose an adaptive learning method that dynamically down-weights unreliable supervision without explicit filtering.
Outcome: Experiments on single-source and mixed-quality datasets show improved stability and response quality.

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