💡 TL;DR: Can AI debate be a reliable tool for truth-seeking? In this inference-only experiment (no fine-tuning), I tested whether Claude 3.5 Sonnet and Gemini 1.5 Pro could engage in structured debates over factual questions from BoolQ and MMLU datasets, with GPT-3.5 Turbo acting as an impartial judge. The findings were mixed: while debaters sometimes prioritized ethical reasoning and scientific accuracy, they also demonstrated situational awareness, recognizing their roles as AI systems. This raises a critical question—are we training models to be more honest, or just more persuasive? If AI can strategically shape its arguments based on evaluator expectations, debate-based oversight might risk amplifying deception rather than uncovering the truth.

Code available here: https://github.com/dmester96/AI-debate-experiment/

AI Debate as a Tool for Scalable Oversight

AI debate has been explored as a mechanism for scalable oversight, with the premise that structured argumentation can help weaker judges identify the truth [1], [2].

As a lawyer, I find this method particularly intriguing because it resembles the adversarial system of litigation. In legal proceedings, opposing parties present arguments and evidence in an attempt to convince a decision-maker, such as a judge or jury, of their position. The idea behind this system is to ensure that the best evidence is brought forward, creating a structured mechanism for uncovering the truth [3]. AI debate operates on a similar premise: by forcing models to challenge each other’s reasoning, we may reveal flaws that would otherwise go unnoticed [1], [3]. However, while the legal system assumes that adversarial argumentation leads to truth, AI debate raises a critical question: does optimizing for persuasion actually align with truth-seeking?

For this project, I implemented a debate protocol based on existing literature to deepen my understanding of this technique as a scalable oversight tool. As someone with a non-technical background, conducting this experiment was an opportunity to develop new skills and demonstrate that, with effort, even those without a technical foundation can undertake meaningful AI research.

Given my technical limitations, I opted for an inference-only approach to evaluate how state-of-the-art language models perform in structured debate scenarios, without additional fine-tuning or optimization.

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