A core part of AI alignment is training AI systems to be helpful, or more generally, to interact with humans appropriately. We look at this problem in the context of large language models. Past works have focused on training these models to perform specific tasks, or follow instructions. In contrast, we believe helpfulness requires back-and-forth interaction between the AI and the human it is trying to assist. Here, we consider a multi-step interaction in which a human asks a question, and the AI has an opportunity to ask a clarifying question to resolve ambiguities before responding. The assistance framework formalizes the idea of an AI which aims to maximize the human's reward but is ignorant of the human reward function. Past works solved toy assistance environments using exact POMDP solvers as well as deep reinforcement learning. We apply a behavioral cloning approach, and fine-tune GPT-3 such that it can respond to clear input questions directly, clarify the intent behind vague input questions, and respond based on the clarification it receives. We show that this approach leads to quantitative improvements in answer accuracy compared to a baseline that cannot ask for clarifications. While the assistance framework assumes the correct behavior of an AI is to infer and maximize a human's reward, our approach can be used to learn any interaction protocol between the AI and the human. We believe exploring interaction protocols that are easy to learn robustly, and can be used to "bootstrap" further alignment are a promising direction for future research.