There is considerable confusion about the role of Large Language Models (LLMs) in planning and reasoning tasks. On one side are over-optimistic claims that LLMs can indeed do these tasks with just the right prompting or self-verification strategies. On the other side are perhaps over-pessimistic claims that all that LLMs are good for in planning/reasoning tasks are as mere translators of the problem specification from one syntactic format to another, and ship the problem off to external symbolic solvers. In this position paper, we take the view that both these extremes are misguided. We argue that auto-regressive LLMs cannot, by themselves, do planning or self-verification (which is after all a form of reasoning), and shed some light on the reasons for misunderstandings in the literature. We will also argue that LLMs should be viewed as universal approximate knowledge sources that have much more meaningful roles to play in planning/reasoning tasks beyond simple front-end/back-end format translators. We present a vision of {\bf LLM-Modulo Frameworks} that combine the strengths of LLMs with external model-based verifiers in a tighter bi-directional interaction regime. We will show how the models driving the external verifiers themselves can be acquired with the help of LLMs. We will also argue that rather than simply pipelining LLMs and symbolic components, this LLM-Modulo Framework provides a better neuro-symbolic approach that offers tighter integration between LLMs and symbolic components, and allows extending the scope of model-based planning/reasoning regimes towards more flexible knowledge, problem and preference specifications.

大语言模型在规划和推理任务中的作用有很多混淆。我们认为，自回归LLM本身无法进行规划或自验证，并指出了文献中常见误解的原因。我们提出了LLM-模块化框架的愿景，将LLM的优势与外部基于模型的验证器在更紧密的双向交互方式中结合起来。我们将展示如何利用LLM获得驱动外部验证器的模型。我们还将论述LLM-模块化框架提供了更好的神经符号方法，通过更灵活的知识、问题和偏好规范，扩展了基于模型的规划/推理范围。

LLMs无法策划，但可以在LLM-Modulo框架中帮助策划