  Chain-of-thought (COT) prompting can help large language models (LLMs) reason
toward correct answers, but its efficacy in reasoning toward incorrect answers
is unexplored. This process of elimination (PoE), when used with COT, can
enhance self-consistency, interpretability, and tasks such as medical diagnoses
of exclusion. Thus, we propose PoE with COT, where LLMs must reason toward
incorrect options on multiple-choice questions. We evaluate the ability of
GPT-3.5, LLaMA-2, and Falcon to perform PoE with COT on a total of four
commonsense and scientific reasoning datasets. We find that the strategy of PoE
always underperforms the strategy of choosing the correct answer. The agreement
of these strategies is also lower than the self-consistency of each strategy.
To study these issues further, we conduct error analyses and give suggestions
for future work.
