  Conversational Information Seeking has evolved rapidly in the last few years
with the development of Large Language Models providing the basis for
interpreting and responding in a naturalistic manner to user requests. iKAT
emphasizes the creation and research of conversational search agents that adapt
responses based on the user's prior interactions and present context. This
means that the same question might yield varied answers, contingent on the
user's profile and preferences. The challenge lies in enabling Conversational
Search Agents (CSA) to incorporate personalized context to effectively guide
users through the relevant information to them. iKAT's first year attracted
seven teams and a total of 24 runs. Most of the runs leveraged Large Language
Models (LLMs) in their pipelines, with a few focusing on a
generate-then-retrieve approach.
