  Language models (LMs) have recently been shown to generate more factual
responses by employing modularity (Zhou et al., 2021) in combination with
retrieval (Adolphs et al., 2021). We extend the recent approach of Adolphs et
al. (2021) to include internet search as a module. Our SeeKeR (Search
engine->Knowledge->Response) method thus applies a single LM to three modular
tasks in succession: search, generating knowledge, and generating a final
response. We show that, when using SeeKeR as a dialogue model, it outperforms
the state-of-the-art model BlenderBot 2 (Chen et al., 2021) on open-domain
knowledge-grounded conversations for the same number of parameters, in terms of
consistency, knowledge and per-turn engagingness. SeeKeR applied to topical
prompt completions as a standard language model outperforms GPT2 (Radford et
al., 2019) and GPT3 (Brown et al., 2020) in terms of factuality and topicality,
despite GPT3 being a vastly larger model. Our code and models are made publicly
available.
