Inverse Scaling: When Bigger Isn't Better
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.
648fb75da466fc93030a7816
Ian R. McKenzie
extracted_confirmed
2024-01-08T23:24:24.706Z
659c83da1b89affadb5c8fa0
/avatars/463e64d013c79df0ccfae7a4a8308996.svg
Ian McKenzie
user
ianmckenzie
648fb75da466fc93030a7817
Alexander Lyzhov
648fb75da466fc93030a7818
Michael Pieler
admin_assigned
2023-06-19T11:17:47.217Z
6037f54f770949ef34f12d99
https://cdn-avatars.huggingface.co/v1/production/uploads/1614280013027-noauth.jpeg
Michael Pieler
user
MicPie
648fb75da466fc93030a7819
Alicia Parrish
claimed_verified
2024-02-07T20:41:25.466Z
659d71eaf0ae1a57ae140831
/avatars/b9eb8c70b4fe5dbd0f1555602ae41b65.svg
Alicia Parrish
user
avparrish
648fb75da466fc93030a781a
Aaron Mueller
admin_assigned
2023-06-19T11:19:01.992Z
60a5abbc8b5faf05f3c4d757
/avatars/eba6c3e61a7a6a12f487d531cf7a9934.svg
Aaron Mueller
user
amueller
648fb75da466fc93030a781b
Ameya Prabhu
admin_assigned
2023-06-19T11:19:58.082Z
6464a0d41683d3c81f51924a
https://cdn-avatars.huggingface.co/v1/production/uploads/6464a0d41683d3c81f51924a/s7yYVwfUB4WOhVFJS6A6T.jpeg
Ameya Prabhu
user
AmeyaPrabhu
648fb75da466fc93030a781c
Euan McLean
648fb75da466fc93030a781d
Aaron Kirtland
648fb75da466fc93030a781e
Alexis Ross
admin_assigned
2023-06-19T11:22:08.754Z
60d39b6d07da9c17c7270916
/avatars/97a0550078e870b5bab82d2e1c6f0597.svg
Alexis Ross
user
alexisjihyeross
648fb75da466fc93030a781f
Alisa Liu
admin_assigned
2023-06-19T11:22:43.044Z
62449da46b55ef458d430371
/avatars/b95954a88c7b15524a3d7776a4abe083.svg
Alisa Liu
user
alisawuffles
648fb75da466fc93030a7820
Andrew Gritsevskiy
admin_assigned
2023-06-19T11:23:31.608Z
635dde5806a944e347c4a5f1
/avatars/382b5f95168efdc9a526ab44cc917b0f.svg
Andrew Gritsevskiy
user
ag8
648fb75da466fc93030a7821
Daniel Wurgaft
claimed_verified
2025-06-30T14:04:07.134Z
662838fe4a0dc08679dfe06d
https://cdn-avatars.huggingface.co/v1/production/uploads/662838fe4a0dc08679dfe06d/xd71eX59Tu5ATxRnYD07V.png
Daniel Wurgaft
user
DanielWurgaft
648fb75da466fc93030a7822
Derik Kauffman
648fb75da466fc93030a7823
Gabriel Recchia
648fb75da466fc93030a7824
Jiacheng Liu
admin_assigned
2023-06-19T11:26:31.941Z
635f46d1928a42bc95cfcf7c
https://cdn-avatars.huggingface.co/v1/production/uploads/635f46d1928a42bc95cfcf7c/5KF8aLiDCJdl7B1SdJ-7V.png
Jiacheng Liu
user
liujch1998
648fb75da466fc93030a7825
Joe Cavanagh
648fb75da466fc93030a7826
Max Weiss
648fb75da466fc93030a7827
Sicong Huang
648fb75da466fc93030a7828
The Floating Droid
648fb75da466fc93030a7829
Tom Tseng
admin_assigned
2023-06-19T11:25:09.748Z
6337c80fb575b33cf0265c76
/avatars/5ab65ef68a38f53d24a7ac9e8bf9d400.svg
Tom Tseng
user
tomtseng
648fb75da466fc93030a782a
Tomasz Korbak
claimed_verified
2023-06-19T15:18:25.542Z
62321bada14cbe4c600b5996
https://cdn-avatars.huggingface.co/v1/production/uploads/1647451045170-noauth.jpeg
Tomek Korbak
user
tomekkorbak
648fb75da466fc93030a782b
Xudong Shen
claimed_verified
2023-06-22T08:14:50.635Z
63c628610be43255f36f2a40
https://cdn-avatars.huggingface.co/v1/production/uploads/1673930799006-noauth.jpeg
Xudong Shen
user
XudongShen
648fb75da466fc93030a782c
Yuhui Zhang
extracted_confirmed
2023-12-06T02:59:26.765Z
62da55164398e21bf7f0e292
https://cdn-avatars.huggingface.co/v1/production/uploads/62da55164398e21bf7f0e292/xjKkG8IA2IZZqCdjApSh3.jpeg
Yuhui Zhang
user
yuhuizhang
648fb75da466fc93030a782d
Zhengping Zhou
648fb75da466fc93030a782e
Najoung Kim
claimed_verified
2024-04-22T06:56:23.271Z
627c0b0a7e62b4bf5c37ea65
/avatars/7b459b6828bcdfd66bc3fbd922606d14.svg
Najoung Kim
user
najoungkim
648fb75da466fc93030a782f
Samuel R. Bowman
648fb75da466fc93030a7830
Ethan Perez
extracted_pending
2024-09-22T23:11:48.381Z
62bb28dc4d3faf9c8938f742
/avatars/cceeb6b8e679b7460519024cd5e301b5.svg
Ethan
user
ethanjperez
Inverse Scaling: When Bigger Isn't Better
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.
Empirical evidence suggests that large language models may show inverse scaling in performance on certain tasks, leading to the importance of careful consideration of training data and objectives.
large language models inverse scaling training objective data preference memorized sequences in-context instructions imitation undesirable patterns distractor task few-shot demonstrations U-shaped scaling inverted-U scaling trends