The advent of recent Natural Language Processing technology has led human and machine
interactions more toward conversation. In Conversational Search Systems (CSS) like …

Conversational information seeking (CIS) is concerned with a sequence of interactions
between one or more users and an information system. Interactions in CIS are primarily …

Retrieval-Augmented Generation (RAG) is a technique that enhances the capabilities of
large language models (LLMs) by incorporating external knowledge sources. This method …

Dense retrieval (DR) has the potential to resolve the query understanding challenge in
conversational search by matching in the learned embedding space. However, this …

We propose Generation-Augmented Retrieval (GAR) for answering open-domain questions,
which augments a query through text generation of heuristically discovered relevant …

Many important questions (eg" How to eat healthier?") require conversation to establish
context and explore in depth. However, conversational question answering (ConvQA) …

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

Conversational question answering (QA) requires the ability to correctly interpret a question
in the context of previous conversation turns. We address the conversational QA task by …

In this work, we introduce ChatQA, a suite of models that outperform GPT-4 on retrieval-
augmented generation (RAG) and conversational question answering (QA). To enhance …

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 …