title: Inverse Scaling: When Bigger Isn't Better
summary: Work on scaling laws has found that large language models (LMs) show
  predictable improvements to overall loss with increased scale (model size,
  training data, and compute). Here, we present evidence for the claim that LMs
  may show inverse scaling, or worse task performance with increased scale, e.g.,
  due to flaws in the training objective and data. We present empirical evidence
  of inverse scaling on 11 datasets collected by running a public contest, the
  Inverse Scaling Prize, with a substantial prize pool. Through analysis of the
  datasets, along with other examples found in the literature, we identify four
  potential causes of inverse scaling: (i) preference to repeat memorized
  sequences over following in-context instructions, (ii) imitation of undesirable
  patterns in the training data, (iii) tasks containing an easy distractor task
  which LMs could focus on, rather than the harder real task, and (iv) correct
  but misleading few-shot demonstrations of the task. We release the winning
  datasets at https://inversescaling.com/data to allow for further investigation
  of inverse scaling. Our tasks have helped drive the discovery of U-shaped and
  inverted-U scaling trends, where an initial trend reverses, suggesting that
  scaling trends are less reliable at predicting the behavior of larger-scale
  models than previously understood. Overall, our results suggest that there are
  tasks for which increased model scale alone may not lead to progress, and that
  more careful thought needs to go into the data and objectives for training
  language models.
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paper_title: Inverse Scaling: When Bigger Isn't Better
paper_summary: Work on scaling laws has found that large language models (LMs) show
  predictable improvements to overall loss with increased scale (model size,
  training data, and compute). Here, we present evidence for the claim that LMs
  may show inverse scaling, or worse task performance with increased scale, e.g.,
  due to flaws in the training objective and data. We present empirical evidence
  of inverse scaling on 11 datasets collected by running a public contest, the
  Inverse Scaling Prize, with a substantial prize pool. Through analysis of the
  datasets, along with other examples found in the literature, we identify four
  potential causes of inverse scaling: (i) preference to repeat memorized
  sequences over following in-context instructions, (ii) imitation of undesirable
  patterns in the training data, (iii) tasks containing an easy distractor task
  which LMs could focus on, rather than the harder real task, and (iv) correct
  but misleading few-shot demonstrations of the task. We release the winning
  datasets at https://inversescaling.com/data to allow for further investigation
  of inverse scaling. Our tasks have helped drive the discovery of U-shaped and
  inverted-U scaling trends, where an initial trend reverses, suggesting that
  scaling trends are less reliable at predicting the behavior of larger-scale
  models than previously understood. Overall, our results suggest that there are
  tasks for which increased model scale alone may not lead to progress, and that
  more careful thought needs to go into the data and objectives for training
  language models.
paper_ai_keywords: large language models (LMs) overall loss model size training data compute inverse scaling task performance training objective data public contest inverse scaling prize preference to repeat memorized sequences following in-context instructions imitation of undesirable patterns easy distractor task wrong real task few-shot demonstrations U-shaped scaling trends inverted-U scaling trends