Simple synthetic data reduces sycophancy in large language models
Authors: name: Jerry Wei, name: Da Huang, name: Yifeng Lu, name: Denny Zhou, name: Quoc V. Le

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.