Data Exploration is an incremental process that helps users express what they want through a conversation with the data. Reinforcement Learning (RL) is one of the most notable approaches to automate data exploration and several solutions have been proposed. We first summarize some RL solutions that were built for different applications. In this context, various data exploration operators are leveraged including traditional roll-up and drill-down operations and text-based operations. An RL agent is trained to generate the best policy according to a hand-crafted reward function.

The benefit of training RL policies for specific data exploration tasks has been demonstrated more than once for exploring finding a needle in a haystack, for serendipitous galaxy exploration, for helping a customer land on a satisfactory product, for helping a conference chair build a program committee in a stepwise fashion, for summarizing large datasets, etc.

With the advent of Large Language Models and their ability to reason sequentially, it has become legitimate to ask the question: would LLMs and AI planning outperform an RL policy in data exploration? More specifically, would LLMs help circumvent retraining for new tasks and striking a balance between specificity and generality? This led us to designing LLM-powered approaches that introduce a new way of thinking about data exploration.