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Training Language Models to Negotiate in the Game of Diplomacy
Mike Lewis

Sat Dec 03 12:45 PM -- 01:30 PM (PST) @

Despite much progress in training AI systems to imitate human language, building agents that use language to communicate intentionally with humans in interactive environments remains a major challenge. I will describe how we adapted language models to negotiate with people, reaching human-level performance in Diplomacy. A typical game involves generating hundreds of messages, which must be grounded in the game state, dialogue history, and the agent’s intended actions - all in a domain far from the pre-training data. The core of our approach is a method for linking language models to a symbolic planning module. Across 40 games of an anonymous online Diplomacy league, Cicero achieved more than double the average score of the human players and ranked in the top 10% of participants who played more than one game.

Author Information

Mike Lewis (FAIR)

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