AI RESEARCH
SIEVE: Sample-Efficient Parametric Learning from Natural Language
arXiv CS.LG
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ArXi:2604.02339v1 Announce Type: new Natural language context-such as instructions, knowledge, or feedback-contains rich signal for adapting language models. While in-context learning provides adaptation via the prompt, parametric learning persists into model weights and can improve performance further, though is data hungry and heavily relies on either high-quality traces or automated verifiers. We propose SIEVE, a method for sample-efficient parametric learning from natural language context that requires as few as three query examples.