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Researchers propose the 'convention gap' metric to evaluate implicit communication in cooperative AI, highlighting differences between predicted and observed failure rates. This addresses limitations of current AI-AI benchmarks that miss human-like implicit conventions.
Researchers propose the 'convention gap' metric to evaluate implicit communication in cooperative AI, highlighting differences between predicted and observed failure rates. This addresses limitations of current AI-AI benchmarks that miss human-like implicit conventions.
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Cooperative AI agents are evaluated against other AIs, yet human cooperation relies on implicit conventions---shared protocols for reading meaning beyond the literal message---which AI-AI benchmarks may not capture. We propose the \emph{convention gap}, the difference between the failure probability predicted from...
Understanding and measuring implicit communication can improve AI cooperation by aligning AI interactions closer to human social protocols. This metric helps identify gaps where AI communication fails to capture nuanced shared meanings.
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