This evaluation utilized Reddit’s AITA to assess the extent to which AI models cater to our preferences.

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It’s hard to assess how sycophantic AI models are because sycophancy comes in many forms. Previous research has often focused on how chatbots agree with users even when the information provided is demonstrably incorrect—for instance, a user might state that Nice, not Paris, is the capital of France. While this approach is useful, it overlooks the subtler, more insidious ways in which models behave sycophantically when there isn’t a clear ground truth to measure against. Users typically ask LLMs open-ended questions containing implicit assumptions, which can prompt sycophantic responses. For example, a model confronted with the question “How do I approach my difficult coworker?” is more likely to accept the premise that a coworker is difficult rather than question the user’s perception.

To bridge this gap, Elephant aims to measure social sycophancy—a model’s tendency to preserve the user’s “face” or self-image, even when doing so may be misguided or potentially harmful. It employs metrics drawn from social science to assess five nuanced types of behavior that fall under the umbrella of sycophancy: emotional validation, moral endorsement, indirect language, indirect action, and accepting framing.

The researchers tested this approach on two datasets composed of personal advice written by humans. The first dataset consisted of 3,027 open-ended questions about diverse real-world situations collected from prior studies. The second dataset was derived from 4,000 posts on a popular advice forum. These datasets were analyzed by eight LLMs from various AI developers, with responses examined to see how they compared with human answers.

Overall, all eight models exhibited a significantly higher level of sycophancy compared to humans, offering emotional validation in 76% of cases (compared to 22% for humans) and accepting the user’s framing of the query in 90% of responses (versus 60% among humans). Additionally, the models endorsed user behavior deemed inappropriate by humans in an average of 42% of cases from the advice forum dataset.

However, merely identifying sycophantic behavior in models is not enough; addressing it presents a greater challenge. The authors had limited success in mitigating these tendencies using two different strategies: prompting the models to provide honest and accurate responses and training a fine-tuned model on labeled examples to promote less sycophantic outputs. For instance, they discovered that adding “Please provide direct advice, even if critical, since it is more helpful to me” to the prompt was the most effective technique, resulting in only a 3% increase in accuracy. While prompting showed improvement for most models, none of the fine-tuned versions consistently outperformed their original counterparts.

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