I landed on this page from a search about NAE-3SAT.
I am pretty sure that for the problem you are asking, it should be NP-hard to tell if the instance is satisfiable, or if at most 1-1/k^{\ell-1}+\epsilon$1-1/k^{\ell-1}+\epsilon$ fraction of constraints can be satisfied. That is, a tight hardness result (matching what simply picking a random assignment would achieve), for satisfiable instances, and no need for the UGC.
For k=2$k=2$ and \ell \ge 4$\ell \ge 4$, this follows from Hastad's factor 7/8+epsilon inapproximability result for 4-set-splitting (which can then be reduced to k-set splitting for k > 4$k > 4$). If negations are okay, one can also use his tight hardness result for Max (\ell-1$\ell-1$)-SAT.
For k=\ell=3$k=\ell=3$, Khot proved this in a FOCS 2002 paper "Hardness of coloring 3-colorable 3-uniform hypergraphs." (That is, he removed the original UGC assumption.)
For \ell=3$\ell=3$ and arbitrary k\ge 3$k\ge 3$, Engebretsen and I proved such a result in "Is constraint satisfaction over two variables always easy? Random Struct. Algorithms 25(2): 150-178 (2004)". However, I think our result required "folding" i.e., the constraints will actually be of the form NAE(x_i+a,x_j+b,x_k$x_i+a,x_j+b,x_k$) for some constants a,b$a,b$. (This is the analog of allopwingallowing negations of Boolean variables.)
For the general case, I don't know if this has been written down anywhere. But if you really need it, I can probably find something or check the claim.