States as Strings as Strategies: Steering Language Models with Game-Theoretic Solvers

• URL: http://arxiv.org/abs/2402.01704v2
• Date: Tue, 6 Feb 2024 08:53:11 GMT
• Title: States as Strings as Strategies: Steering Language Models with Game-Theoretic Solvers
• Authors: Ian Gemp, Yoram Bachrach, Marc Lanctot, Roma Patel, Vibhavari Dasagi, Luke Marris, Georgios Piliouras, Siqi Liu, Karl Tuyls
• Abstract summary: A suitable model of the players, strategies, and payoffs associated with linguistic interactions would enable existing game-theoretic algorithms to provide strategic solutions in the space of language. We present one possible binding from dialogue to game theory as well as generalizations of existing equilibrium finding algorithms to this setting.
• Score: 44.64118885012762
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Game theory is the study of mathematical models of strategic interactions among rational agents. Language is a key medium of interaction for humans, though it has historically proven difficult to model dialogue and its strategic motivations mathematically. A suitable model of the players, strategies, and payoffs associated with linguistic interactions (i.e., a binding to the conventional symbolic logic of game theory) would enable existing game-theoretic algorithms to provide strategic solutions in the space of language. In other words, a binding could provide a route to computing stable, rational conversational strategies in dialogue. Large language models (LLMs) have arguably reached a point where their generative capabilities can enable realistic, human-like simulations of natural dialogue. By prompting them in various ways, we can steer their responses towards different output utterances. Leveraging the expressivity of natural language, LLMs can also help us quickly generate new dialogue scenarios, which are grounded in real world applications. In this work, we present one possible binding from dialogue to game theory as well as generalizations of existing equilibrium finding algorithms to this setting. In addition, by exploiting LLMs generation capabilities along with our proposed binding, we can synthesize a large repository of formally-defined games in which one can study and test game-theoretic solution concepts. We also demonstrate how one can combine LLM-driven game generation, game-theoretic solvers, and imitation learning to construct a process for improving the strategic capabilities of LLMs.

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