  I shall quantify the logical faults generated by ChatGPT when applied to
reasoning tasks. For experiments, I use the 144 puzzles from the library
\url{https://users.utcluj.ro/~agroza/puzzles/maloga}~\cite{groza:fol}. The
library contains puzzles of various types, including arithmetic puzzles,
logical equations, Sudoku-like puzzles, zebra-like puzzles, truth-telling
puzzles, grid puzzles, strange numbers, or self-reference puzzles. The correct
solutions for these puzzles were checked using the theorem prover
Prover9~\cite{mccune2005release} and the finite models finder
Mace4~\cite{mccune2003mace4} based on human-modelling in Equational First Order
Logic. A first output of this study is the benchmark of 100 logical puzzles.
For this dataset ChatGPT provided both correct answer and justification for 7\%
only. %, while BARD for 5\%. Since the dataset seems challenging, the
researchers are invited to test the dataset on more advanced or tuned models
than ChatGPT3.5 with more crafted prompts. A second output is the
classification of reasoning faults conveyed by ChatGPT. This classification
forms a basis for a taxonomy of reasoning faults generated by large language
models. I have identified 67 such logical faults, among which: inconsistencies,
implication does not hold, unsupported claim, lack of commonsense, wrong
justification. The 100 solutions generated by ChatGPT contain 698 logical
faults. That is on average, 7 fallacies for each reasoning task. A third ouput
is the annotated answers of the ChatGPT with the corresponding logical faults.
Each wrong statement within the ChatGPT answer was manually annotated, aiming
to quantify the amount of faulty text generated by the language model. On
average, 26.03\% from the generated text was a logical fault.
