Introduces P-CHR AUC and CRR metrics to demonstrate that semantic caching model selection is limited by calibration quality rather than ranking performance.
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2026 2verdicts
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DataEvolver introduces a multi-level self-evolving system for automatic data preparation that improves LLM performance by an average of 10% over original data on seven benchmarks.
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Closing the Calibration Gap in Semantic Caching
Introduces P-CHR AUC and CRR metrics to demonstrate that semantic caching model selection is limited by calibration quality rather than ranking performance.
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DataEvolver: Automatic Data Preparation for Large Language Models through Multi-Level Self-Evolving
DataEvolver introduces a multi-level self-evolving system for automatic data preparation that improves LLM performance by an average of 10% over original data on seven benchmarks.