Multiple Choice Questions: Reasoning Makes Large Language Models (LLMs)
  More Self-Confident Even When They Are Wrong
Authors: name: Tairan Fu, name: Javier Conde, name: Gonzalo Martínez, name: María Grandury, name: Pedro Reviriego

One of the most widely used methods to evaluate LLMs are Multiple Choice
Question (MCQ) tests. MCQ benchmarks enable the testing of LLM knowledge on
almost any topic at scale as the results can be processed automatically. To
help the LLM answer, a few examples called few shots can be included in the
prompt. Moreover, the LLM can be asked to answer the question directly with the
selected option or to first provide the reasoning and then the selected answer,
which is known as chain of thought. In addition to checking whether the
selected answer is correct, the evaluation can look at the LLM-estimated
probability of its response as an indication of the confidence of the LLM in
the response. In this paper, we study how the LLM confidence in its answer
depends on whether the model has been asked to answer directly or to provide
the reasoning before answering. The results of the evaluation of questions on a
wide range of topics in seven different models show that LLMs are more
confident in their answers when they provide reasoning before the answer. This
occurs regardless of whether the selected answer is correct. Our hypothesis is
that this behavior is due to the reasoning that modifies the probability of the
selected answer, as the LLM predicts the answer based on the input question and
the reasoning that supports the selection made. Therefore, LLM estimated
probabilities seem to have intrinsic limitations that should be understood in
order to use them in evaluation procedures. Interestingly, the same behavior
has been observed in humans, for whom explaining an answer increases confidence
in its correctness.