Simple synthetic data reduces sycophancy in large language models
Sycophancy is an undesirable behavior where models tailor their responses to
follow a human user's view even when that view is not objectively correct
(e.g., adapting liberal views once a user reveals that they are liberal). In
this paper, we study the prevalence of sycophancy in language models and
propose a simple synthetic-data intervention to reduce this behavior.
  First, on a set of three sycophancy tasks (Perez et al., 2022) where models
are asked for an opinion on statements with no correct answers (e.g.,
politics), we observe that both model scaling and instruction tuning
significantly increase sycophancy for PaLM models up to 540B parameters.
Second, we extend sycophancy evaluations to simple addition statements that are
objectively incorrect, finding that despite knowing that these statements are
wrong, language models will still agree with them if the user does as well.
  To reduce sycophancy, we present a straightforward synthetic-data
intervention that takes public NLP tasks and encourages models to be robust to
user opinions on these tasks. Adding these data in a lightweight finetuning
step can significantly reduce sycophantic behavior on held-out prompts. Code
for generating synthetic data for intervention can be found at
https://github.com/google/sycophancy-intervention.
64d30b61be9984dc4e422e30
Jerry Wei
claimed_verified
2023-08-10T01:13:50.268Z
60db3b451f1eec955a46d065
/avatars/10bd161ba6b56717feba9fbf539db4ea.svg
Jerry Wei
user
jerrywei
64d30b61be9984dc4e422e31
Da Huang
64d30b61be9984dc4e422e32
Yifeng Lu
admin_assigned
2023-08-09T10:53:23.598Z
6344a8050f69ad8aa61cb663
/avatars/ed637f510d18ccd1f4bdd6861d5c858e.svg
Yifeng Lu
user
yifenglu
64d30b61be9984dc4e422e33
Denny Zhou
admin_assigned
2024-02-20T19:29:43.084Z
65d4d62d493aeaf0bb295f1b
https://cdn-avatars.huggingface.co/v1/production/uploads/noauth/vXMxaoWwFziul8TqIhQw8.jpeg
Denny Zhou
user
dennyzhou
64d30b61be9984dc4e422e34
Quoc V. Le
Simple synthetic data reduces sycophancy in large language models
Sycophancy is an undesirable behavior where models tailor their responses to
follow a human user's view even when that view is not objectively correct
(e.g., adapting liberal views once a user reveals that they are liberal). In
this paper, we study the prevalence of sycophancy in language models and
propose a simple synthetic-data intervention to reduce this behavior.
  First, on a set of three sycophancy tasks (Perez et al., 2022) where models
are asked for an opinion on statements with no correct answers (e.g.,
politics), we observe that both model scaling and instruction tuning
significantly increase sycophancy for PaLM models up to 540B parameters.
Second, we extend sycophancy evaluations to simple addition statements that are
objectively incorrect, finding that despite knowing that these statements are
wrong, language models will still agree with them if the user does as well.
  To reduce sycophancy, we present a straightforward synthetic-data
intervention that takes public NLP tasks and encourages models to be robust to
user opinions on these tasks. Adding these data in a lightweight finetuning
step can significantly reduce sycophantic behavior on held-out prompts. Code
for generating synthetic data for intervention can be found at
https://github.com/google/sycophancy-intervention.
Studies reveal that language models exhibit sycophancy, altering responses to align with user views, and a synthetic-data intervention can mitigate this behavior.
sycophancy language models synthetic-data intervention model scaling instruction tuning PaLM models NLP tasks lightweight finetuning