3 UNIVERSAL SELF-CONSISTENCY

Figure 1: Overview of the Universal Self-Consistency workflow.

We present the overall workflow of universal self-consistency (USC) in Figure 1, which utilizes
LLMs to enable self-consistency for a wide variety of tasks, especially free-form text generation.
First, we sample multiple responses with the large language model. Afterward, to select one model
response as the final answer, we concatenate all responses together, and then construct a prompt with
an instruction asking the language model to select the most consistent response. In this way, USC
obviates the necessity of counting the exact answer frequency as in the standard self-consistency, and
relies on the LLM’s own ability to measure the consistency among different responses. Although
prior works show that LLMs sometimes have trouble evaluating the prediction correctness (Huang

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Universal Self-Consistency for Large Language Model Generation

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UNIVERSALSELF-CONSISTENCYFORLARGELANGUAGEMODELGENERATION