Recent years have witnessed a substantial increase in the use of deep learning to solve
various natural language processing (NLP) problems. Early deep learning models were …

Rapid growth in the generation of data from various sources has made data visualisation a
valuable tool for analysing data. However, visual analysis can be a challenging task, not …

Large language models (LLMs) powered conversational search systems have already been
used by hundreds of millions of people, and are believed to bring many benefits over …

Query rewriting plays a vital role in enhancing conversational search by transforming context-
dependent user queries into standalone forms. Existing approaches primarily leverage …

A long-standing challenge for search and conversational assistants is query intention
detection in ambiguous queries. Asking clarifying questions in conversational search has …

Conversational search has seen increased recent attention in both the IR and NLP
communities. It seeks to clarify and solve users' search needs through multi-turn natural …

Chat-based language models are designed to be helpful, yet they should not comply with
every user request. While most existing work primarily focuses on refusal of" unsafe" …

Evaluating conversational information retrieval (CIR) systems is a challenging task that
requires a significant amount of human labor for annotation. It is imperative to invest …

Users often ask dialogue systems ambiguous questions that require clarification. We show
that current language models rarely ask users to clarify ambiguous questions and instead …

We introduce a novel framework named ClarifyGPT, which aims to enhance code
generation by empowering LLMs with the ability to identify ambiguous requirements and ask …