Synthetic Dialogue Dataset Generation using LLM Agents

Understand

Linear programming (LP) problems are pervasive in real-life applications.

• However, despite their apparent simplicity, an untrained user may find it difficult to determine the linear model of their specific problem.
• We envisage the creation of a goal-oriented conversational agent that will engage in conversation with the user to elicit all information required so that a subsequent agent can generate the linear model.
• In this paper, we present an approach for the generation of sample dialogues that can be used to develop and train such a conversational agent.