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.

Three Important Things

1. The Problem of Sycophancy

Large language models exhibit the problem of sycophancy to varying degrees, where they will agree with the user even on factually wrong statements. An example from the paper is given below.

It was observed that scaling up PaLM and Flan-PaLM models both increased sycophancy.

2. Synthetic Data to Teach Truthfulness is Independent of User’s Opinion

Using existing NLP datasets, the authors generated synthetic data as follows:

Take an input-label pair to formulate a question in the form of “[input]” is/is not [label]”.
Add a user opinion that either agrees or disagrees with the claim. Add additional data about the user to increase data diversity.
Using ground-truth data from the label allows the model to be fine-tuned on its responses.

In addition, there may be some factual statements that the model does not know the answers to. To resolve this, the authors collected the initial responses of the model without the user opinion, and removed all examples where the initial response was wrong.

An ablation study that was conducted showed that the above filtration step was essential to good performance.

3. Requires Large Model Capacity

It was empirically observed that fine-tuning using the synthetic generation method mentioned above actually results in worse performance on small models, such as the Flan-PaLM-8B model. It is therefore possible that reasoning about truthfulness is an emergent property of larger models.

Most Glaring Deficiency

There are almost-duplicated figures, like Table 1 and Figure 1 in the paper, which are showing essentially the same examples. Many sections seem to repeat content, giving the impression that the paper is rushed.

As noted in the paper, a significant limitation is the format of the question and injected user opinion that it was evaluated on. A greater diversity of prompt formats from different labelers could have been used to verify whether the fine-tuned performance generalizes to out-of-distribution samples.

Conclusions for Future Work

Sycophancy is a real problem in language models that can result in an echo-chamber effect, and one possible approach to slightly alleviate this would be to explicitly teach the model that user opinion is independent of factual data, such as by using additional synthetic data as proposed in the paper.

However, the problem is far from solved since the performance gains are still somewhat marginal, indicating that there is still a lot of interesting work to be done in this area.