Iris: A Conversational Agent for Complex Tasks
This addresses the problem of performing complex data science tasks more efficiently for users, representing a novel method for a known bottleneck rather than a foundational advancement.
The paper tackles the limitation of conversational agents to simple standalone commands by introducing Iris, an agent that uses human conversational strategies to combine commands for complex tasks, resulting in a 2.6 times speedup for data scientists in predictive modeling tasks compared to non-conversational environments.
Today's conversational agents are restricted to simple standalone commands. In this paper, we present Iris, an agent that draws on human conversational strategies to combine commands, allowing it to perform more complex tasks that it has not been explicitly designed to support: for example, composing one command to "plot a histogram" with another to first "log-transform the data". To enable this complexity, we introduce a domain specific language that transforms commands into automata that Iris can compose, sequence, and execute dynamically by interacting with a user through natural language, as well as a conversational type system that manages what kinds of commands can be combined. We have designed Iris to help users with data science tasks, a domain that requires support for command combination. In evaluation, we find that data scientists complete a predictive modeling task significantly faster (2.6 times speedup) with Iris than a modern non-conversational programming environment. Iris supports the same kinds of commands as today's agents, but empowers users to weave together these commands to accomplish complex goals.