  Claim verification is an essential step in the automated fact-checking
pipeline which assesses the veracity of a claim against a piece of evidence. In
this work, we explore the potential of few-shot claim verification, where only
very limited data is available for supervision. We propose MAPLE (Micro
Analysis of Pairwise Language Evolution), a pioneering approach that explores
the alignment between a claim and its evidence with a small seq2seq model and a
novel semantic measure. Its innovative utilization of micro language evolution
path leverages unlabelled pairwise data to facilitate claim verification while
imposing low demand on data annotations and computing resources. MAPLE
demonstrates significant performance improvements over SOTA baselines SEED, PET
and LLaMA 2 across three fact-checking datasets: FEVER, Climate FEVER, and
SciFact. Data and code are available here: https://github.com/XiaZeng0223/MAPLE
