← 研踪 PaperTrailACL Anthology · ACL 2026:acl:2026.acl-long.515 · 记录 #1
PIAST: Rapid Prompting with In-context Augmentation for Scarce Training data
来源日期（仅到月份） 2026-07
来源记录日期 2026-07
雷达观测 2026-11-01 22:02
摘要
LLMs are highly sensitive to prompt design, but handcrafting effective prompts is difficult and often requires intricate crafting of few-shot examples. We propose a fast automatic prompt construction algorithm that augments human instructions by generating a small set of few shot examples. Our method iteratively replaces/drops/keeps few-shot examples using Monte Carlo Shapley estimation of example utility. For faster execution, we use aggressive subsampling and a replay buffer for faster evaluations. Our method can be run using different compute time budgets. Under a limited budget, it outperforms prior automatic prompting methods on text simplification and mathematical reasoning (GSM8K, DeepMath, Math500), while achieving second-best results on classification and summarization and third-best on MedQA. With an extended, yet still modest budget, PIAST sets a new state of the art among automatic prompting methods on classification, simplification, GSM8K, DeepMath, and Math500. Overall, our results suggest that optimizing in-context examples, rather than exhaustively searching over instruction rewrites is the dominant lever for fast and data-efficient prompt engineering. We will release code and data upon acceptance.
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记录 #1 · 2026-07 · 最新观测版本
PIAST: Rapid Prompting with In-context Augmentation for Scarce Training data
LLMs are highly sensitive to prompt design, but handcrafting effective prompts is difficult and often requires intricate crafting of few-shot examples. We propose a fast automatic prompt construction algorithm that augments human instructions by generating a small set of few shot examples. Our method iteratively replaces/drops/keeps few-shot examples using Monte Carlo Shapley estimation of example utility. For faster execution, we use aggressive subsampling and a replay buffer for faster evaluations. Our method can be run using different compute time budgets. Under a limited budget, it outperforms prior automatic prompting methods on text simplification and mathematical reasoning (GSM8K, DeepMath, Math500), while achieving second-best results on classification and summarization and third-best on MedQA. With an extended, yet still modest budget, PIAST sets a new state of the art among automatic prompting methods on classification, simplification, GSM8K, DeepMath, and Math500. Overall, our results suggest that optimizing in-context examples, rather than exhaustively searching over instruction rewrites is the dominant lever for fast and data-efficient prompt engineering. We will release code and data upon acceptance.
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