  We study the problem of online unweighted bipartite matching with $n$ offline
vertices and $n$ online vertices where one wishes to be competitive against the
optimal offline algorithm. While the classic RANKING algorithm of Karp et al.
[1990] provably attains competitive ratio of $1-1/e > 1/2$, we show that no
learning-augmented method can be both 1-consistent and strictly better than
$1/2$-robust under the adversarial arrival model. Meanwhile, under the random
arrival model, we show how one can utilize methods from distribution testing to
design an algorithm that takes in external advice about the online vertices and
provably achieves competitive ratio interpolating between any ratio attainable
by advice-free methods and the optimal ratio of 1, depending on the advice
quality.
