  With its origin coming from several sources (Big Bang, stars, cosmic rays)
and given its strong depletion during its stellar lifetime, the lithium element
is of great interest as its chemical evolution in the Milky Way is not well
understood at present. To help constrain stellar and galactic chemical
evolution models, numerous and precise lithium abundances are necessary for a
large range of evolutionary stages, metallicities, and Galactic volume. In the
age of stellar parametrization on industrial scales, spectroscopic surveys such
as APOGEE, GALAH, RAVE, and LAMOST have used data-driven methods to rapidly and
precisely infer stellar labels (atmospheric parameters and abundances). To
prepare the ground for future spectroscopic surveys such as 4MOST and WEAVE, we
aim to apply machine learning techniques to lithium measurements and analyses.
We trained a convolution neural network (CNN), coupling Gaia-ESO Survey iDR6
stellar labels (Teff, log(g), [Fe/H], and A(Li)) and GIRAFFE HR15N spectra, to
infer the atm parameters and lithium abundances for ~40,000 stars. We show that
the CNN properly learns the physics of the stellar labels, from relevant
spectral features through a broad range of evolutionary stages and stellar
parameters. The Li feature at 6707.8 A is successfully singled out by our CNN,
among the thousands of lines. Rare objects such as Li-rich giants are found in
our sample. This level of performance is achieved thanks to a meticulously
built, high-quality, and homogeneous training sample. The CNN approach is very
well adapted for the next generations of spectroscopic surveys aimed at
studying (among other elements) lithium, such as the 4MIDABLE-LR/HR (4MOST
Milky Way disk and bulge low- and high-resolution) surveys. In this context,
the caveats of ML applications should be appropriately investigated, along with
the realistic label uncertainties and upper limits for abundances.
