ShinkaEvolve improves sample efficiency in LLM-driven program evolution via parent sampling, code novelty rejection-sampling, and bandit LLM ensemble selection, achieving new SOTA circle packing with 150 samples and gains on math reasoning and competitive programming tasks.
To ensemble or not: Assessing majority voting strategies for phishing detection with large language models, 2024
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A learning-to-defer framework allocates extractive QA queries to LLM experts with theoretical optimality guarantees, shown to improve reliability and cut overhead on SQuAD and TriviaQA.
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ShinkaEvolve: Towards Open-Ended And Sample-Efficient Program Evolution
ShinkaEvolve improves sample efficiency in LLM-driven program evolution via parent sampling, code novelty rejection-sampling, and bandit LLM ensemble selection, achieving new SOTA circle packing with 150 samples and gains on math reasoning and competitive programming tasks.
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Optimal Query Allocation in Extractive QA with LLMs: A Learning-to-Defer Framework with Theoretical Guarantees
A learning-to-defer framework allocates extractive QA queries to LLM experts with theoretical optimality guarantees, shown to improve reliability and cut overhead on SQuAD and TriviaQA.