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LLM-Judged Policy Debates Aim to Cut Through Conventional Wisdom
A new framework uses an LLM as judge in policy debates, forcing the model to confront expert knowledge over three rounds. The goal: reduce the grip of conventional wisdom on policy analysis.
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A new proposal for resolving policy disputes would hand the verdict to a large language model, not a human panel. The idea: two debaters submit analyses over three rounds, each time pointing the LLM to expert studies that conflict with the model's default conventional wisdom. The LLM then updates its position based on the evidence, not the political leanings of the participants.
The framework was outlined in a paper posted on arXiv this week. Under the system, each debater starts with a short position statement. The LLM issues its own initial position. Then the debaters offer detailed analyses citing expert sources. The LLM responds with a new analysis and position. The cycle repeats twice more, and the LLM delivers a final verdict.
The rationale is that experts who point out flaws in conventional wisdom are often accused of bias. LLMs, at least for now, face less of that accusation. By forcing the model to reconcile its own stored knowledge with specific expert results, the iterative process may surface conflicts that conventional debate formats miss.