Inverse Scaling: When Bigger Isn't Better
Ian R. McKenzie Alexander Lyzhov Michael Pieler Alicia Parrish Aaron Mueller Ameya Prabhu Euan McLean Aaron Kirtland Alexis Ross Alisa Liu Andrew Gritsevskiy Daniel Wurgaft Derik Kauffman Gabriel Recchia Jiacheng Liu Joe Cavanagh Max Weiss Sicong Huang The Floating Droid Tom Tseng Tomasz Korbak Xudong Shen Yuhui Zhang Zhengping Zhou Najoung Kim Samuel R. Bowman Ethan Perez
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.