Steering Language Models with Game-Theoretic Solvers

Abstract

Mathematical models of strategic interactions among rational agents have long been studied in game theory. However the interactions studied are often over a small set of discrete 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 a dialogue task in terms of the players, strategies and payoffs of the ``game" of dialogue, we can create a binding from natural language interactions to the conventional symbolic logic of game theory. Given this binding, we can ask existing game-theoretic algorithms to provide us with strategic solutions (e.g., what string an LLM should generate to maximize payoff at equilibrium), giving us predictors of stable, rational conversational strategies that current LLMs can employ when generating dialogue. We focus on three domains that require different negotiation strategies: scheduling meetings, trading fruit and debate, and evaluate a state-of-the-art pre-trained LLM's ability to generate language when guided by solvers. Our evaluation assesses whether LLMs are more strategic against their partners when guided by equilibrium solvers and whether the language generated under these solutions results in higher payoff. We see that LLMs that do follow game-theory solvers result in dialogue generations that are less exploitable than the control (no guidance from solvers) in our three negotiation domains. We discuss future implications of this work, and how game-theoretic solvers that can leverage the expressivity of natural language can open up a new avenue of guiding language research.