Large Language Model (LLM) Emergent Property

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An Large Language Model (LLM) Emergent Property is an emergent property for LLMs that manifests as new capabilities or improvements in performance not evident in smaller-scale models, often appearing abruptly as the model size increases.

  • Context:
    • It can (typically) involve LLM Tasks that smaller models struggle with.
    • It can (often) be influenced by the choice of LLM Evaluation Metric, with nonlinear or discontinuous metrics potentially exaggerating the appearance of emergent properties.
    • It can (often) be observed across different LLM Architectures.
    • It can provoke discussions on the optimal ways to measure and understand model performance, emphasizing the need for metrics that accurately reflect incremental improvements.
    • ...
  • Example(s):
    • The transition from GPT-2 to GPT-3 demonstrated emergent abilities in generating coherent long texts, showcasing qualitative differences in language generation capabilities.
    • In vision tasks, altering the evaluation metrics can induce perceptions of emergent abilities, showing how metric choice can affect interpretations of model capability.
    • ...
  • Counter-Example(s):
  • See: Emergent System, Model Scaling, LLM Evaluation Measure.


References

2023

2023

  • https://windowsontheory.org/2023/12/22/emergent-abilities-and-grokking-fundamental-mirage-or-both/
    • NOTES
      • Emergent abilities in large language models: As models get bigger, they can suddenly gain new capabilities not seen in smaller models. For example, GPT-2 could generate coherent long text while GPT could not.
      • Scale and unpredictable jumps in performance: As models scale up, their performance often jumps sharply at some point in an unpredictable way. For example, models may go from trivial performance to perfect accuracy on a task as compute is increased.
      • Are emergent abilities a mirage? Recent work has shown these jumps can disappear if we change the evaluation metric to a softer one. For example, instead of binary accuracy, using edit distance as the metric can show more gradual progress.
      • Analogous to high jump example: An athlete's max jump height increases smoothly with training, but probability of clearing a fixed bar jumps sharply at some point.
      • Sharp transitions remain for complex tasks: Even if components improve smoothly, the probability of succeeding at multiple steps sequentially can still transition sharply from low to high.
      • Main conclusion: Emergent abilities are likely not a complete mirage, since many real-world tasks require multiple steps of reasoning where failing at one step derails you. So sharp unpredictable transitions in capabilities are likely to remain.

2023

2022