Conformal Relevance Framework via In-Context Ensembles
September 1, 2026
The Conformal Relevance framework replaces manual prompt engineering for scoring functions in NLP tasks like summarization. It utilizes in-context learning example curation and ensembling to optimize the trade-off between information coverage and conciseness.
HOW THIS AFFECTS YOU
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builderYou can reduce the manual effort required to tune prompts for extractive tasks like summarization.
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researcherYou can leverage ensembling and curated examples to automate score function design in conformal prediction.