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Recent advances in large-scale pre-training such as GPT-3 allow seeminglyhigh quality text to be generated from a given prompt. However, such generationsystems often suffer from problems of hallucinated facts, and are notinherently designed to incorporate useful external information. Groundedgeneration models appear to offer remedies, but their training typically relieson rarely-available parallel data where information-relevant documents areprovided for context. We propose a framework that alleviates this dataconstraint by jointly training a grounded generator and document retriever onthe language model signal. The model learns to reward retrieval of thedocuments with the highest utility in generation, and attentively combines themusing a Mixture-of-Experts (MoE) ensemble to generate follow-on text. Wedemonstrate that both generator and retriever can take advantage of this jointtraining and work synergistically to produce more informative and relevant textin both prose and dialogue generation.

RetGen: A Joint framework for Retrieval and Grounded Text Generation Modeling