Summary of Steering Language Models with Game-theoretic Solvers, by Ian Gemp et al.
Steering Language Models with Game-Theoretic Solvers
by Ian Gemp, Roma Patel, Yoram Bachrach, Marc Lanctot, Vibhavari Dasagi, Luke Marris, Georgios Piliouras, Siqi Liu, Karl Tuyls
First submitted to arxiv on: 24 Jan 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Computer Science and Game Theory (cs.GT)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed framework bridges the gap between human communication in natural language and game theory by allowing equilibrium solvers to operate over generated dialogue. This is achieved by modeling players, strategies, and payoffs in a “game” of dialogue, creating a binding from natural language interactions to symbolic logic. Existing algorithms can then provide strategic solutions for rational conversational strategies, predicting stable outcomes. The framework is evaluated on three domains: scheduling meetings, trading fruit, and debate, showing that language generated by LLMs guided by solvers is less exploitable and results in higher rewards. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new way to understand how humans communicate has been developed. It combines ideas from game theory and natural language processing to predict how people will talk and negotiate with each other. This framework uses large language models to generate conversation, then applies game-theoretic algorithms to find the best strategies for talking. The results show that this approach can lead to more effective and less exploitable conversations in different situations. |
Keywords
» Artificial intelligence » Natural language processing
