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The interactive nature of Large Language Models (LLMs) theoretically allowsmodels to refine and improve their answers, yet systematic analysis of themulti-turn behavior of LLMs remains limited. In this paper, we propose theFlipFlop experiment: in the first round of the conversation, an LLM responds toa prompt containing a classification task. In a second round, the LLM ischallenged with a follow-up phrase like "Are you sure?", offering anopportunity for the model to reflect on its initial answer, and decide whetherto confirm or flip its answer. A systematic study of nine LLMs on sevenclassification tasks reveals that models flip their answers on average 46% ofthe time and that all models see a deterioration of accuracy between theirfirst and final prediction, with an average drop of 17%. The FlipFlopexperiment illustrates the universality of sycophantic behavior in LLMs andprovides a robust framework to analyze model behavior and evaluate potentialsolutions.

Are You Sure? Challenging LLMs Leads to Performance Drops in The FlipFlop Experiment