You don't gain anything by throwing away information, so the most elegant solution is to maintain the ordering inherent in each 7-level Likert item by treating each of them as a 7-level ordinal predictor. In R these are called ordered factors.
Although you don't know that each of the 6 steps between levels for each Likert item has the same magnitude with respect to predicting your outcome variable, you do presumably suspect that each higher step is more strongly related to outcome. Hence each item is appropriately considered a multi-level ordinal variable.
When a factor is specified as an ordered predictor in R it is modeled as a set of orthogonal polynomials. That, however, converts each 7-level Likert item into 6 predictors, so there is a danger of overfitting if you have multiple Likert items and only a moderate-sized data set.
This page discusses other approaches to using ordinal variables as predictors, with links to further discussion. In particular, the
ordSmooth() function in the R package ordPens smooths ordinal predictors with penalization to lessen the chance of overfitting while taking advantage of the ordering of the levels,