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arXiv Machine Learning By Xiao Shi Huang, Chen-Yuan Lin, Bruce Kuwahara, Kin Kwan Leung, Jesse C. Cresswell

Unifying Conformal Language Tasks with In-Context Ensembles

Read the original on arXiv Machine Learning →

The paper introduces the Conformal Relevance framework, which employs in-context learning example curation and ensembling to generate a score function that preserves coverage while enhancing conciseness for NLP tasks such as summarization and extractive question answering. Unlike previous methods that rely on labor-intensive, task‑specific prompt engineering, this approach requires minimal manual input. The authors validate the framework across seven NLP tasks and provide a theoretical analysis of how diversity in ensembled conformal scores can improve worst‑case sentence scores, including a saturation bound on ensemble gains.

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