Verifiers are auxiliary models that assess the correctness of outputs generated by base large language models (LLMs). They play a crucial role in many strategies for solving reasoning-intensive problems with LLMs. Typically, verifiers are LLMs themselves, often as large (or larger) than the base model they support, making them computationally expensive. In this work, we introduce a novel lightweight verification approach, LiLaVe, which reliably extracts correctness signals from the hidden states of the base LLM. A key advantage of LiLaVe is its ability to operate with only a small fraction of the computational budget required by traditional LLM-based verifiers. To demonstrate its practicality, we couple LiLaVe with popular meta-generation strategies, like best-of-n or self-consistency. Moreover, we design novel LiLaVe-based approaches, like conditional self-correction or conditional majority voting, that significantly improve both accuracy and efficiency in generation tasks with smaller LLMs. Our work demonstrates the fruitfulness of extracting latent information from the hidden states of LLMs, and opens the door to scalable and resource-efficient solutions for reasoning-intensive applications.

本研究解决了现有的大语言模型（LLM）验证器通常计算开销大、资源消耗高的问题。我们提出了一种新的轻量级验证方法LiLaVe，该方法能够从基础LLM的隐藏状态中提取正确性信号，仅需传统验证器小部分的计算资源。研究表明LiLaVe在与流行的元生成策略结合时，显著提高了生成任务的准确性和效率，为推理密集型应用提供了可扩展和高效的解决方案。

轻量级潜在验证器用于高效的元生成策略