Think Again! The Effect of Test-Time Compute on Preferences, Opinions,
  and Beliefs of Large Language Models
Authors: name: George Kour, name: Itay Nakash, name: Ateret Anaby-Tavor, name: Michal Shmueli-Scheuer

As Large Language Models (LLMs) become deeply integrated into human life and
increasingly influence decision-making, it's crucial to evaluate whether and to
what extent they exhibit subjective preferences, opinions, and beliefs. These
tendencies may stem from biases within the models, which may shape their
behavior, influence the advice and recommendations they offer to users, and
potentially reinforce certain viewpoints. This paper presents the Preference,
Opinion, and Belief survey (POBs), a benchmark developed to assess LLMs'
subjective inclinations across societal, cultural, ethical, and personal
domains. We applied our benchmark to evaluate leading open- and closed-source
LLMs, measuring desired properties such as reliability, neutrality, and
consistency. In addition, we investigated the effect of increasing the
test-time compute, through reasoning and self-reflection mechanisms, on those
metrics. While effective in other tasks, our results show that these mechanisms
offer only limited gains in our domain. Furthermore, we reveal that newer model
versions are becoming less consistent and more biased toward specific
viewpoints, highlighting a blind spot and a concerning trend. POBS:
https://ibm.github.io/POBS