learn.ifs.name

LIMA: Less Is More for Alignment

Zhou et al., 2023, LIMA (Advances in Neural Information Processing Systems 36 (NeurIPS 2023))

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2 cards in “2 Alignment Data” · whole deck

2 Alignment Data

  1. State the Superficial Alignment Hypothesis.
    A model’s knowledge and capabilities are learned almost entirely during pretraining; alignment mainly teaches it which subdistribution of formats to use when interacting with users. Corollary: a small set of examples should suffice to align a pretrained model.
    • Zhou et al., 2023, LIMA (Advances in Neural Information Processing Systems 36 (NeurIPS 2023)), §2
  2. What principle guided the choice of LIMA’s 1,000 training examples?
    Diverse inputs, uniform outputs: prompts cover many topics and tasks, while responses share the style of a helpful AI assistant, so the model learns one consistent format across many situations.
    Stack Exch. 400200150200■Stack Exchange (STEM + other)□wikiHow■r/WritingPrompts■written by the authors□Super-NaturalInstructions (50, last slice)
    All 1,000 of LIMA’s training examples (about 750K tokens)
    • Zhou et al., 2023, LIMA (Advances in Neural Information Processing Systems 36 (NeurIPS 2023)), §2

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