  Conversational information seeking has evolved rapidly in the last few years
with the development of Large Language Models (LLMs), providing the basis for
interpreting and responding in a naturalistic manner to user requests. The
extended TREC Interactive Knowledge Assistance Track (iKAT) collection aims to
enable researchers to test and evaluate their Conversational Search Agents
(CSA). The collection contains a set of 36 personalized dialogues over 20
different topics each coupled with a Personal Text Knowledge Base (PTKB) that
defines the bespoke user personas. A total of 344 turns with approximately
26,000 passages are provided as assessments on relevance, as well as additional
assessments on generated responses over four key dimensions: relevance,
completeness, groundedness, and naturalness. The collection challenges CSA to
efficiently navigate diverse personal contexts, elicit pertinent persona
information, and employ context for relevant conversations. The integration of
a PTKB and the emphasis on decisional search tasks contribute to the uniqueness
of this test collection, making it an essential benchmark for advancing
research in conversational and interactive knowledge assistants.
