The emergence of generative artificial intelligence (GenAI) is transforming
information interaction. For decades, search engines such as Google and Bing
have been the primary means of locating relevant information for the general
population. They have provided search results in the same standard format (the
so-called "10 blue links"). The recent ability to chat via natural language
with AI-based agents and have GenAI automatically synthesize answers in
real-time (grounded in top-ranked results) is changing how people interact with
and consume information at massive scale. These two information interaction
modalities (traditional search and AI-powered chat) coexist in current search
engines, either loosely coupled (e.g., as separate options/tabs) or tightly
coupled (e.g., integrated as a chat answer embedded directly within a
traditional search result page). We believe that the existence of these two
different modalities, and potentially many others, is creating an opportunity
to re-imagine the search experience, capitalize on the strengths of many
modalities, and develop systems and strategies to support seamless flow between
them. We refer to these as panmodal experiences. Unlike monomodal experiences,
where only one modality is available and/or used for the task at hand, panmodal
experiences make multiple modalities available to users (multimodal), directly
support transitions between modalities (crossmodal), and seamlessly combine
modalities to tailor task assistance (transmodal). While our focus is search
and chat, with learnings from insights from a survey of over 100 individuals
who have recently performed common tasks on these two modalities, we also
present a more general vision for the future of information interaction using
multiple modalities and the emergent capabilities of GenAI.

生成式人工智能正在改变信息交互，对搜索引擎进行全面的改进以支持多种搜索和聊天交互方式是一个重要研究领域。

泛模态信息交互

Panmodal Information Interaction

Knowledge graph construction (KGC) is a multifaceted undertaking involving
the extraction of entities, relations, and events. Traditionally, large
language models (LLMs) have been viewed as solitary task-solving agents in this
complex landscape. However, this paper challenges this paradigm by introducing
a novel framework, CooperKGC. Departing from the conventional approach,
CooperKGC establishes a collaborative processing network, assembling a KGC
collaboration team capable of concurrently addressing entity, relation, and
event extraction tasks. Our experiments unequivocally demonstrate that
fostering collaboration and information interaction among diverse agents within
CooperKGC yields superior results compared to individual cognitive processes
operating in isolation. Importantly, our findings reveal that the collaboration
facilitated by CooperKGC enhances knowledge selection, correction, and
aggregation capabilities across multiple rounds of interactions.

CooperKGC 是一个新颖的框架，通过建立协作处理网络，同时解决实体、关系和事件提取任务，展示合作和信息交互在知识选择、纠正和聚合方面的优越性。