  Generations from large language models (LLMs) can be improved by sampling and
scoring multiple solutions to select a final answer. Current "sample and
select" methods such as self-consistency (SC) rely on majority voting to score
answers. However, when tasks have many distinct and valid answers, selection by
voting requires a large number of samples. This makes SC prohibitively
expensive for interactive tasks that involve generating multiple actions
(answers) sequentially. After establishing that majority voting fails to
provide consistent gains on such tasks, we demonstrate how to increase success
rates by softening the scoring criterion. We introduce Soft Self-Consistency
(SOFT-SC), which replaces SC's discontinuous scoring with a continuous score
computed from model likelihoods, allowing for selection even when actions are
sparsely distributed. SOFT-SC improves both performance and efficiency on
long-horizon interactive tasks, requiring half as many samples as SC for
comparable or better performance. For a fixed number of samples, SOFT-SC leads
to a 1.3% increase over SC in absolute success rate on writing bash programs, a
6.6% increase on online shopping (WebShop), and a 4.7% increase for an
interactive household game (ALFWorld). Finally, we show that SOFT-SC can be
applied to both open-source and black-box models.
