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Context. 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. Aims. 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. Methods. 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 atmospheric parameters and lithium abundances for ∼40 000 stars. The CNN architecture and accompanying notebooks are available online via GitHub. Results. 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 lithium feature at 6707.8 Å is successfully singled out by our CNN, among the thousands of lines in the GIRAFFE HR15N setup. Rare objects such as lithium-rich giants are found in our sample. This level of performance is achieved thanks to a meticulously built, high-quality, and homogeneous training sample. Conclusions. 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 machine-learning applications should be appropriately investigated, along with the realistic label uncertainties and upper limits for abundances. [ABSTRACT FROM AUTHOR]

We propose here that the lithium decrease at super-solar metallicities observed in high-resolution spectroscopic surveys can be explained by the interplay of mixed populations that originate in the inner regions of the Milky Way disk. The lower lithium content of these stars is a consequence of inside-out disk formation plus radial migration. In this framework, local stars with super-solar metallicities would have migrated to the solar vicinity and depleted their original lithium during their travel time. To obtain this result, we took advantage of the AMBRE catalog of lithium abundances combined with chemical evolution models that take into account the contribution to the lithium enrichment by different nucleosynthetic sources. A large proportion of migrated stars can explain the observed lower lithium abundance at super-solar metallicities. We stress that no stellar model is currently able to predict Li-depletion for these super-solar metallicity stars, and solar Li-depletion has to be assumed. In addition, no solid quantitative estimate of the proportion of migrated stars in the solar neighborhood and their travel time is currently available. Our results illustrate how important it is to properly include radial migration when chemical evolution models are compared to observations, and that in this case, the lithium decrease at larger metallicities does not necessarily imply that stellar yields have to be modified, contrary to previous claims in the literature. [ABSTRACT FROM AUTHOR]