Tag
This paper introduces Target-Weighted Neyman Allocation (TWNA), a two-stage stratified experimental design that optimizes sample allocation across groups and treatment arms to improve precision of target-weighted group average treatment effects under population shift.
This paper presents a framework for opinion summarization using LLMs that combines multidimensional classification and stratified sampling to reduce token usage while preserving semantic diversity and balance across viewpoints.
Proposes SLAP, a novel data selection framework for efficient instruction tuning of large language models that evaluates batch learnability and uses stratified sampling to achieve superior performance with 20-40% less training data.