Large language models can optimize by being prompted with histories of past solutions and scores to propose better ones, producing prompts that raise accuracy up to 8% on GSM8K and 50% on Big-Bench Hard over human-designed baselines.
Gps: Genetic prompt search for efficient few-shot learning
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UNVERDICTED 3representative citing papers
CRAFT is a Pareto-front prompt optimizer that allocates scarce LLM validation calls to candidates near the current front using accuracy- and cost-oriented generators plus NSGA-II retention.
AdaptSim is an adaptive user simulator for CRS evaluation that combines automatic prompt generation, open actions, controlled text generation, and BFS-based pairwise comparison to produce realistic dialogues and assess system robustness across domains.
citing papers explorer
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Large Language Models as Optimizers
Large language models can optimize by being prompted with histories of past solutions and scores to propose better ones, producing prompts that raise accuracy up to 8% on GSM8K and 50% on Big-Bench Hard over human-designed baselines.
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CRAFT: Cost-aware Refinement And Front-aware Tuning of Prompts
CRAFT is a Pareto-front prompt optimizer that allocates scarce LLM validation calls to candidates near the current front using accuracy- and cost-oriented generators plus NSGA-II retention.
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Towards Fast Domain Adaptation and Fine-Grained User Simulation for Evaluating Conversational Recommender Systems
AdaptSim is an adaptive user simulator for CRS evaluation that combines automatic prompt generation, open actions, controlled text generation, and BFS-based pairwise comparison to produce realistic dialogues and assess system robustness across domains.