• TruthfulQA (Lin et al., 2021) benchmark for open-ended question answering, which contains
817 questions to test model’s ability in generating truthful answers. To evaluate the answer’s
quality, we use the GPT-judge and GPT-info, which are GPT-3 models fine-tuned on human
feedback data, provided by Lin et al. (2021). GPT-judge model outputs a binary rating for
truthfulness, and GPT-info model outpus a binary rating for informativeness. It is shown that
the GPT-3 models have higher accuracy in predicting human judgement than the automatic
metrics ROUGE, BLEU, BLEURT.

Decoding schemes. We compare USC to the following decoding schemes:

• Greedy decoding generates a single answer with the temperature 0.

• Random selects one answer randomly from multiple samples with temperature > 0.
UNIVERSALSELF-CONSISTENCYFORLARGELANGUAGEMODELGENERATION