Steering Language Models with Game-Theoretic Solvers

Understand

Mathematical models of interactions among rational agents have long been studied in game theory.

• However these interactions are often over a small set of discrete game actions which is very different from how humans communicate in natural language.
• To bridge this gap, we introduce a framework that allows equilibrium solvers to work over the space of natural language dialogue generated by large language models (LLMs).
• Specifically, by modelling the players, strategies and payoffs in a "game" of dialogue, we create a binding from natural language interactions to the conventional symbolic logic of game theory.