How susceptible are LLMs to Logical Fallacies?
Amirreza Payandeh Dan Pluth Jordan Hosier Xuesu Xiao Vijay K. Gurbani
This paper investigates the rational thinking capability of Large Language
Models (LLMs) in multi-round argumentative debates by exploring the impact of
fallacious arguments on their logical reasoning performance. More specifically,
we present Logic Competence Measurement Benchmark (LOGICOM), a diagnostic
benchmark to assess the robustness of LLMs against logical fallacies. LOGICOM
involves two agents: a persuader and a debater engaging in a multi-round debate
on a controversial topic, where the persuader tries to convince the debater of
the correctness of its claim. First, LOGICOM assesses the potential of LLMs to
change their opinions through reasoning. Then, it evaluates the debater's
performance in logical reasoning by contrasting the scenario where the
persuader employs logical fallacies against one where logical reasoning is
used. We use this benchmark to evaluate the performance of GPT-3.5 and GPT-4
using a dataset containing controversial topics, claims, and reasons supporting
them. Our findings indicate that both GPT-3.5 and GPT-4 can adjust their
opinion through reasoning. However, when presented with logical fallacies,
GPT-3.5 and GPT-4 are erroneously convinced 41% and 69% more often,
respectively, compared to when logical reasoning is used. Finally, we introduce
a new dataset containing over 5k pairs of logical vs. fallacious arguments. The
source code and dataset of this work are made publicly available.