Large language models may or may not be adequate for certain reasoning and planning tasks, depending on the task category and specifications, environment, user interaction, and other factors. On one hand, LLMs are designed for modelling human language in general, instead of accurate modelling and logical processing, and while reasoning and planning is a specific kind of task that can involve language, the models may not be very suitable for them out of the box. Special-purpose algorithms and software, such as SMT solvers, can be more efficient and suitable for many problems in practice. On the other hand, LLMs' generality and flexibility can be a strength, particularly for open-ended problem domains where the problem specification is unclear and human feedback is needed. Humans naturally make heavy use of language to aid their reasoning and planning, and there are large amounts of examples of these tasks in natural language corpus, so it's natural that LLMs can perform many reasoning and planning-related tasks when given appropriate prompts and examples. Furthermore, there is recent development in this area using chain-of-thought, reflection, and other tools to emulate human thinking strategies and work around the limitations of current large language models, that proved to be very useful in improving performance. However, there is still much to discover about how AI systems including LLMs can reason and plan, and there's no guarantee that current systems will be acceptable in a given scenario with its particular scope of problems, given the risks and rewards for all stakeholders. In the end, whether LLMs are adequate for reasoning and planning depends on how you plan to use them, and while they are not currently the best fit for all problems, chances are they can do a decent amount of tasks if people can provide necessary human input and tolerate some uncertainties.
spam fails_task lang_mismatch pii not_appropriate hate_speech sexual_content quality toxicity humor helpfulness creativity violence