Abstract
Tasks are central to information retrieval (IR) and drive interactions with search systems [2, 4, 10]. Understanding and modeling tasks helps these systems better support user needs [8, 9, 11]. This keynote focuses on search tasks, the emergence of generative artificial intelligence (AI), and the implications of recent work at their intersection for the future of search. Recent estimates suggest that half of Web search queries go unanswered, many of them connected to complex search tasks that are ill-defined or multi-step and span several queries[6]. AI copilots, e.g., ChatGPT and Bing Chat, are emerging to address complex search tasks and many other challenges. These copilots are built on large foundation models such as GPT-4 and are being extended with skills and plugins. Copilots broaden the surface of tasks achievable via search, moving toward creation not just finding (e.g., interview preparation, email composition), and can make searchers more efficient and more successful.
Users currently engage with AI copilots via natural language queries and dialog and the copilots generate answers with source attribution [7]. However, in delegating responsibility for answer generation, searchers also lose some control over aspects of the search process, such as directly manipulating queries and examining lists of search results [1]. The efficiency gains from auto-generating a single, synthesized answer may also reduce opportunities for user learning and serendipity. A wholesale move to copilots for all search tasks is neither practical nor necessary: model inference is expensive, conversational interfaces are unfamiliar to many users in a search context, and traditional search already excels for many types of task. Instead, experiences that unite search and chat are becoming more common, enabling users to adjust the modality and other aspects (e.g., answer tone) based on the task.
The rise of AI copilots creates many opportunities for IR, including aligning generated answers with user intent, tasks, and applications via human feedback [3]; understanding copilot usage, including functional fixedness [5]; using context and data to tailor responses to people and situations (e.g., grounding, personalization); new search experiences (e.g., unifying search and chat); reliability and safety (e.g., accuracy, bias); understanding impacts on user learning and agency; and evaluation (e.g., model-based feedback, searcher simulations [12] repeatability). Research in these and related areas will enable search systems to more effectively utilize new copilot technologies together with traditional search to help searchers better tackle a wider variety of tasks.
