arXiv (OAI Expanded)
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Failure-Guided Co-Evolution of Prompts and Training Data
Automatic prompt optimization (APO) improves language-model programs by revising prompts from task feedback, yet it typically holds its training data fixed. Repeatedly optimizing against the same instances confines feedback to weaknesses already represented in those data, leaving related failure con
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