STELLAR-E modifies the TGRT Self-Instruct framework to produce tailored synthetic LLM evaluation datasets that score an average 5.7% higher on LLM-as-a-judge metrics than existing language-specific benchmarks.
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A hypothesis-driven pipeline generates targeted hard math problems that drop Llama-3.3-70B-Instruct accuracy from 77% on MATH to as low as 45%.
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STELLAR-E: a Synthetic, Tailored, End-to-end LLM Application Rigorous Evaluator
STELLAR-E modifies the TGRT Self-Instruct framework to produce tailored synthetic LLM evaluation datasets that score an average 5.7% higher on LLM-as-a-judge metrics than existing language-specific benchmarks.
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Automatically Generating Hard Math Problems from Hypothesis-Driven Error Analysis
A hypothesis-driven pipeline generates targeted hard math problems that drop Llama-3.3-70B-Instruct accuracy from 77% on MATH to as low as 45%.