Large language models for response evaluation. The underlying assumption in our work is that
LLMs are reflective enough to evaluate the consistency between multiple self-generated outputs. This
assumption is related to recent findings which had shown that large language models can also be used
for evaluating model-generated texts (Bubeck et al., 2023; Fu et al., 2023; Wang et al., 2023a; Zhang
et al., 2023b). LLM-based evaluators have demonstrated some promising results, e.g., they can be
used to evaluate natural language generations without human references, but some work has also
shown that they might not correlate very well with human judgements and sometimes exhibit bias
towards model-generated texts (Bubeck et al., 2023; Liu et al., 2023). Another line of work utilizes
the prediction probability of the LLM to measure the quality of multiple choices (Ren et al., 2023;
Adiwardana et al., 2020), and Lin et al. (2022) show promising results on arithmetic tasks where they
UNIVERSALSELF-CONSISTENCYFORLARGELANGUAGEMODELGENERATION