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One challenge with open-domain dialogue systems is the need to producetruthful, high-quality responses on any topic. We aim to improve the qualityand coverage of Athena, an Alexa Prize dialogue system. We experiment withfew-shot prompt-based learning, comparing GPT-Neo to Jurassic-1, for themovies, music, TV, sports, and video game domains, both within andcross-domain, with different prompt set sizes (2, 3, 10), formats, and meaningrepresentations consisting of either sets of WikiData KG triples, or dialogueacts. Our evaluation uses BLEURT and human metrics, and shows that with 10-shotprompting, Athena-Jurassic's performance is significantly better for coherenceand semantic accuracy. Experiments with 2-shot cross-domain prompts results ina huge performance drop for Athena-GPT-Neo, whose semantic accuracy falls to0.41, and whose untrue hallucination rate increases to 12%. Experiments withdialogue acts for video games show that with 10-shot prompting, both modelslearn to control dialogue acts, but Athena-Jurassic has significantly highercoherence, and only 4% untrue hallucinations. Our results suggest thatAthena-Jurassic produces high enough quality outputs to be useful in livesystems with real users. To our knowledge, these are the first resultsdemonstrating that few-shot semantic prompt-based learning can create NLGs thatgeneralize to new domains, and produce high-quality, semantically-controlled,conversational responses directly from meaning representations.

Jurassic is (almost) All You Need: Few-Shot Meaning-to-Text Generation for Open-Domain Dialogue