Large language models (LLMs) face significant challenges stemming from their 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 requires accurate interpretation of user intent from complex multi-turn
contexts. This paper presents ChatRetriever, which inherits the strong generalization …

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 …

Conversational search provides a more convenient interface for users to search by allowing
multi-turn interaction with the search engine. However, the effectiveness of the …

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 utilizes muli-turn natural language contexts to retrieve relevant
passages. Existing conversational dense retrieval models mostly view a conversation as a …

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 …