Large language models (LLMs) face significant challenges stemming from the inherent
limitations in knowledge, memory, alignment, and action. These challenges cannot be …

Precisely understanding users' contextual search intent has been an important challenge for
conversational search. As conversational search sessions are much more diverse and long …

In conversational search, the user's real search intent for the current turn is dependent on
the previous conversation history. It is challenging to determine a good search query from …

Conversational search allows a user to interact with a search system in multiple turns. A
query is strongly dependent on the conversation context. An effective way to improve …

As a cornerstone of modern information access, search engines have become
indispensable in everyday life. With the rapid advancements in AI and natural language …

Conversational search facilitates complex information retrieval by enabling multi-turn
interactions between users and the system. Supporting such interactions requires a …

Conversational search supports multi-turn user-system interactions to solve complex
information needs. Different from the traditional single-turn ad-hoc search, conversational …

We present a Conversational Chain-of-Action (Conv-CoA) framework for Open-domain
Conversational Question Answering (OCQA). Compared with literature, Conv-CoA …

Personalized conversational information retrieval (CIR) combines conversational and
personalizable elements to satisfy various users' complex information needs through multi …

Conversational search, unlike single-turn retrieval tasks, requires understanding the current
question within a dialogue context. The common approach of rewrite-then-retrieve aims to …