Papers with Certainty Gain Maximization

1 papers
Active Learning for Abstractive Text Summarization via LLM-Determined Curriculum and Certainty Gain Maximization (2024.findings-emnlp)

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Challenge: Abstractive text summarization (ATS) requires laborious data annotation and time-consuming model training.
Approach: They propose a novel active learning framework that asks large language models to rate difficulty of instances and then uses certainty gain maximization to select instances with a distribution that aligns well with the overall distribution.
Outcome: The proposed framework improves stability, effectiveness, and efficiency of abstractive text summarization backbones.

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