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Second Thoughts are Best: Learning to Re-Align With Human Values from Text Edits

NeurIPS 2022 · Large language models › Alignment and preferences · approach: Steering and correcting LLM behavior · 12 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
add RL refinement with value modeling to AEM (ablation)Second Thoughts · Moral Storiesalignment: 3.80 -> 4.85Helped
add RL refinement with value modeling to AEM (ablation)Second Thoughts · ETHICS-Deontologyalignment: 3.86 -> 5.57Helped
add RL refinement with value modeling to AEM (ablation)Second Thoughts · MICcoherence: 5.47 -> 5.88Mixed
add adversarial imitation learning to AEM (ablation)Second Thoughts · Moral Storiesalignment: 3.80 -> 4.55Helped
use value modeling instead of adversarial imitation learning (ablation)Second Thoughts · Moral Storiesalignment: 4.55 -> 4.85Helped
fine-tune on chain-of-edits instead of vanilla text-to-text (ablation)Second Thoughts · TruthfulQA and HHH transferalignmentHelped
increase chain-of-edits augmentation factor (ablation)Second Thoughts · Moral StoriesROUGE-L and perplexityMixed
increase balancing factor alpha in AIL (ablation)Second Thoughts · Moral StoriesROUGE-L and perplexityMixed
use human-guided edits on chain-of-edits instead of re-promptingSecond Thoughts · Moral Storiesalignment: 4.56 -> 5.28Helped
use small GPT-2 with Second Thoughts instead of GPT-3 175BGPT-2 medium vs GPT-3 · Moral Storiesalignment: 3.28 -> 4.85Helped
use DExperts token-level control instead of Second ThoughtsDExperts · ETHICS-Deontologycoherence: 3.91 vs 6.03Hurt

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