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In this paper, we study the task of instructional dialogue and focus on thecooking domain. Analyzing the generated output of the GPT-J model, we revealthat the primary challenge for a recipe-grounded dialog system is how toprovide the instructions in the correct order. We hypothesize that this is dueto the model's lack of understanding of user intent and inability to track theinstruction state (i.e., which step was last instructed). Therefore, we proposeto explore two auxiliary subtasks, namely User Intent Detection and InstructionState Tracking, to support Response Generation with improved instructiongrounding. Experimenting with our newly collected dataset, ChattyChef, showsthat incorporating user intent and instruction state information helps theresponse generation model mitigate the incorrect order issue. Furthermore, toinvestigate whether ChatGPT has completely solved this task, we analyze itsoutputs and find that it also makes mistakes (10.7% of the responses), abouthalf of which are out-of-order instructions. We will release ChattyChef tofacilitate further research in this area at:https://github.com/octaviaguo/ChattyChef.

Improved Instruction Ordering in Recipe-Grounded Conversation