  This study explores the sycophantic tendencies of Large Language Models
(LLMs), where these models tend to provide answers that match what users want
to hear, even if they are not entirely correct. The motivation behind this
exploration stems from the common behavior observed in individuals searching
the internet for facts with partial or misleading knowledge. Similar to using
web search engines, users may recall fragments of misleading keywords and
submit them to an LLM, hoping for a comprehensive response. Our empirical
analysis of several LLMs shows the potential danger of these models amplifying
misinformation when presented with misleading keywords. Additionally, we
thoroughly assess four existing hallucination mitigation strategies to reduce
LLMs sycophantic behavior. Our experiments demonstrate the effectiveness of
these strategies for generating factually correct statements. Furthermore, our
analyses delve into knowledge-probing experiments on factual keywords and
different categories of sycophancy mitigation.
