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Agents represent different positions and critically negotiate outcomes before a final decision is synthesized. Typical conditions for use: The problem allows for controversial assessments; Counterarguments help expose logical flaws. The central trade-off: Rigorous verification of difficult decisions is achieved against increased effort and the risk of over-a…
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Theoretical construct: explains a term, principle, or mental model.
What organizes, connects, or makes decisions possible.
Multi-Agent Debate has multiple language-model instances exchange, critique, and refine answers and reasoning over several rounds.
The approach connects debate with ensemble methods and was proposed for language models by Du and colleagues in 2023 to improve factuality and reasoning. Multiple model instances first take positions and then challenge one another.
Imagine three specialists solving independently, exposing their reasoning, and pointing out one another's mistakes. A final instance consolidates the sound arguments; many voices still do not guarantee truth.
Several agents first produce independent answers or solution paths.
Agents inspect the other participants' claims and reasoning.
A procedure combines the reviewed contributions into a final result.
Multi-Agent Debate can make difficult answers more robust, while increasing latency, cost, and the chance of agents confirming the same error.
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