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Research map / Large language models / Alignment and preferences

The Wisdom of Hindsight Makes Language Models Better Instruction Followers

ICML 2023 · Large language models › Alignment and preferences · approach: Reward modeling for diverse preferences · 9 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Use hindsight instruction relabeling rather than FARL's correct-answer-only filteringFLAN-T5-large · Tracking Shuffled Objects (5)accuracy: 15.6 -> 61.2Helped
Use hindsight instruction relabeling rather than FARL's correct-answer-only filteringFLAN-T5-large · Logical Deduction (5 Objects)accuracy: 54.0 -> 67.0Helped
Use hindsight instruction relabeling rather than FARL's correct-answer-only filteringFLAN-T5-large · Word Sortingaccuracy: 3.4 -> 3.4No clear effect
Add sub-output sampling (ablation)HIR · Geometric Shapes; Tracking Shuffled Objects (3); Logical Deduction (3 Objects)accuracy: 86.1 -> 90.3; 100.0 -> 100.0; 75.0 -> 91.7Helped
Add entropy regularization (ablation)HIR · Geometric Shapes; Tracking Shuffled Objects (3); Logical Deduction (3 Objects)accuracy: 47.2 -> 90.3; 100.0 -> 100.0; 48.3 -> 91.7Helped
Add label smoothing (ablation)HIR · Geometric Shapes; Tracking Shuffled Objects (3); Logical Deduction (3 Objects)accuracy: 84.7 -> 90.3; 100.0 -> 100.0; 23.3 -> 91.7Helped

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