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We introduce GODEL (Grounded Open Dialogue Language Model), a largepre-trained language model for dialog. In contrast with earlier models such asDialoGPT, GODEL leverages a new phase of grounded pre-training designed tobetter support adapting GODEL to a wide range of downstream dialog tasks thatrequire information external to the current conversation (e.g., a database ordocument) to produce good responses. Experiments against an array of benchmarksthat encompass task-oriented dialog, conversational QA, and groundedopen-domain dialog show that GODEL outperforms state-of-the-art pre-traineddialog models in few-shot fine-tuning setups, in terms of both human andautomatic evaluation. A novel feature of our evaluation methodology is theintroduction of a notion of utility that assesses the usefulness of responses(extrinsic evaluation) in addition to their communicative features (intrinsicevaluation). We show that extrinsic evaluation offers improved inter-annotatoragreement and correlation with automated metrics. Code and data processingscripts are publicly available.

GODEL: Large-Scale Pre-Training for Goal-Directed Dialog