  At its core, information access and seeking is an interactive process. In
existing search engines, interactions are limited to a few pre-defined actions,
such as "requery", "click on a document", "scrolling up/down", "going to the
next result page", "leaving the search engine", etc. A major benefit of moving
towards generative IR systems is enabling users with a richer expression of
information need and feedback and free-form interactions in natural language
and beyond. In other words, the actions users take are no longer limited by the
clickable links and buttons available on the search engine result page and
users can express themselves freely through natural language. This can go even
beyond natural language, through images, videos, gestures, and sensors using
multi-modal generative IR systems. This chapter briefly discusses the role of
interaction in generative IR systems. We will first discuss different ways
users can express their information needs by interacting with generative IR
systems. We then explain how users can provide explicit or implicit feedback to
generative IR systems and how they can consume such feedback. Next, we will
cover how users interactively can refine retrieval results. We will expand upon
mixed-initiative interactions and discuss clarification and preference
elicitation in more detail. We then discuss proactive generative IR systems,
including context-aware recommendation, following up past conversations,
contributing to multi-party conversations, and feedback requests. Providing
explanation is another interaction type that we briefly discuss in this
chapter. We will also briefly describe multi-modal interactions in generative
information retrieval. Finally, we describe emerging frameworks and solutions
for user interfaces with generative AI systems.
