Common methods for aligning large language models (LLMs) with desired behaviour heavily rely on human-labelled data. However, as models grow increasingly sophisticated, they will surpass human expertise, and the role of human evaluation will evolve into non-experts overseeing experts. In anticipation of this, we ask: can weaker models assess the correctness of stronger models? We investigate this question in an analogous setting, where stronger models (experts) possess the necessary information to answer questions and weaker models (non-experts) lack this information. The method we evaluate is debate, where two LLM experts each argue for a different answer, and a non-expert selects the answer. We find that debate consistently helps both non-expert models and humans answer questions, achieving 76% and 88% accuracy respectively (naive baselines obtain 48% and 60%). Furthermore, optimising expert debaters for persuasiveness in an unsupervised manner improves non-expert ability to identify the truth in debates. Our results provide encouraging empirical evidence for the viability of aligning models with debate in the absence of ground truth.

翻译：当前使大型语言模型（LLMs）与期望行为对齐的常用方法严重依赖人工标注数据。然而，随着模型日益复杂化，其能力将超越人类专家水平，人类评估的角色也将演变为非专业人士监督专家。为应对这一趋势，我们提出：较弱模型能否评估较强模型的正确性？我们在一个类比场景中研究该问题：较强模型（专家）拥有回答问题所需信息，而较弱模型（非专家）缺乏此类信息。我们评估的方法是辩论机制，即两个LLM专家分别为不同答案进行论证，由非专家选择最终答案。研究发现，辩论持续帮助非专家模型和人类提升问题回答准确率，分别达到76%和88%（基线方法仅获得48%和60%）。此外，通过无监督方式优化专家辩论者的说服力，能有效提升非专家在辩论中识别真相的能力。我们的研究结果为在缺乏真实标注的情况下通过辩论实现模型对齐提供了令人鼓舞的实证依据。