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We introduce a new domain expert mixture (DEMix) layer that enablesconditioning a language model (LM) on the domain of the input text. A DEMixlayer is a collection of expert feedforward networks, each specialized to adomain, that makes the LM modular: experts can be mixed, added or removed afterinitial training. Extensive experiments with autoregressive transformer LMs (upto 1.3B parameters) show that DEMix layers reduce test-time perplexity,increase training efficiency, and enable rapid adaptation with little overhead.We show that mixing experts during inference, using a parameter-free weightedensemble, allows the model to better generalize to heterogeneous or unseendomains. We also show that experts can be added to iteratively incorporate newdomains without forgetting older ones, and that experts can be removed torestrict access to unwanted domains, without additional training. Overall,these results demonstrate benefits of explicitly conditioning on textualdomains during language modeling.

DEMix Layers: Disentangling Domains for Modular Language Modeling