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Data visualization is integral to any Exploratory Data Analysis (EDA) task. However, generating visualization requires expertise, presenting a steep learning curve and a significant cognitive load. Natural language interfaces for EDA aim to lower this barrier by allowing users to generate visualizations through natural language queries. However, complexity remains when EDA is performed collaboratively, requiring an environment to support multi-user interaction. In this thesis proposal, we discuss challenges in user-system interaction in a collaborative multi-user setup, such as errors in visualization generation due to misinterpretation of user requests. We hypothesize that a Conversational Assistant (CA) capable of understanding user-initiated clarification requests and generating accurate responses can improve user experience and support collaborative EDA tasks. To this end, we propose to develop such a CA (Figure tab:system_issues) and evaluate it through a user study, thus examining its impact on user experience in a collaborative environment for EDA.