When Large Language Models contradict humans? Large Language Models'
  Sycophantic Behaviour
Leonardo Ranaldi Giulia Pucci
Large Language Models (LLMs) have been demonstrating the ability to solve
complex tasks by delivering answers that are positively evaluated by humans due
in part to the intensive use of human feedback that refines responses. However,
the suggestibility transmitted through human feedback increases the inclination
to produce responses that correspond to the user's beliefs or misleading
prompts as opposed to true facts, a behaviour known as sycophancy. This
phenomenon decreases the bias, robustness, and, consequently, their
reliability.
  In this paper, we shed light on the suggestibility of LLMs to sycophantic
behaviour, demonstrating these tendencies via human-influenced prompts over
different tasks. Our investigation reveals that LLMs show sycophantic
tendencies when responding to queries involving subjective opinions and
statements that should elicit a contrary response based on facts, demonstrating
a lack of robustness.