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STaR: Bootstrapping Reasoning With Reasoning

NeurIPS 2022 · Large language models › Reasoning and chain of thought · approach: Search and self-correction in reasoning · 114 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
add rationalization (answer-hinted rationale generation for failed problems) (ablation)STaR · CommonsenseQA devaccuracy: 68.8 -> 72.5Helped
add rationalization (ablation)STaR · GSM8K testaccuracy: 10.1 -> 10.7No clear effect
STaR rationale bootstrapping vs direct-answer finetuningGPT-J · CommonsenseQA devaccuracy: 60.0 -> 72.5Helped
STaR vs direct-answer finetuningGPT-J · GSM8K testaccuracy: 5.8 -> 10.7Helped
STaR vs direct finetuning without rationalesGPT-J · Arithmeticaccuracy: 76.3% -> 89.5%Helped
include few-shot prompts during fine-tuning (ablation)STaR without rationalization · CommonsenseQA devaccuracy: 60.9% -> 68.8%Helped
include few-shot prompts during fine-tuning (ablation)STaR with rationalization · CommonsenseQA devaccuracy: 69.9% -> 72.5%Helped
use higher sampling temperature (0.5 or 0.7) instead of rationalization (ablation)STaR · Arithmetic / CQAaccuracyHurt

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