Figuring out how to get strong control over the language model by grounding in "intents"/plans was one of the major challenges of this work. Fig. 4 in the paper shows we achieved relatively strong control in this sense: prior to any filters, ~93% of messages generated by CICERO were consistent with intents and ~87% were consistent with the game state. As you note, however, the model is not perfect, and we relied on a suite of classifiers to help filter additional mistakes. Many of the mistakes CICERO made were relative to information that was *not* directly represented in its input (and thus required additional reasoning steps), e.g., reasoning further-into-the-future states or counterfactual past states, discussing plans for third parties, etc. We could have considered grounding CICERO in a richer representation of "intents" (e.g., including plans for third parties) or of the game state (e.g., explicitly representing past states), but in practice we found that (i) richer intents would be harder to annotate/select and often take the language model out of distribution and (ii) we had to balance the trade off between richer game state representation with the dialogue history representation. Exploring ways to get stronger control/improve the reasoning capabilities of language models is an interesting future direction. -ED