Can LLMs Really Reason and Plan? Exploring the Potential of Language Models

Can LLMs Really Reason and Plan? Exploring the Potential of Language Models

Language models (LLMs) have been making significant strides in various domains, showcasing their ability to generate ideas and potential solutions for different tasks, including those involving reasoning and planning. However, the question remains: Can LLMs truly reason and plan autonomously?

While some skeptics argue that LLMs lack the capability for autonomous reasoning and planning, it doesn't necessarily discount their constructive roles in solving such tasks. One of their notable strengths lies in their uncanny ability to generate ideas and potential candidate solutions. This can be particularly valuable in what we call "LLM-Modulo" setups, where LLMs work in conjunction with model-based planners, external solvers, or expert humans in the loop.

The key here is to recognize that LLMs excel at generating potential answers that can be checked and refined by external solvers. It's crucial not to ascribe autonomous reasoning capabilities to LLMs. Frameworks like LangChain are designed with this understanding in mind, using LLMs to generate potential solutions that are then verified by external sources.

To further understand the role of planning in LLMs, researchers have explored the performance of GPT4, a highly advanced language model. By obfuscating the names of actions and objects in planning problems, they sought to reduce the effectiveness of approximate retrieval, a key aspect of planning. Surprisingly, GPT4's empirical performance drastically declined when faced with obfuscated planning problems, despite standard AI planners having no trouble with such obfuscation. In the Blocks World domain, GPT4 reached an empirical accuracy of 30%, further highlighting its limitations in autonomous planning.

However, there are approaches that can mitigate these limitations. One effective method is to incorporate an external model-based plan verifier that performs the back prompting and certifies the correctness of the final solution. This approach ensures that the LLM's generated guesses are thoroughly evaluated and refined by an external source, reducing the chances of relying solely on the LLM's autonomous reasoning.

It's important to note the limitations of certain techniques like CoT (Clever Hans effect) in leveraging LLMs for planning tasks. CoT involves the human in the loop steering the LLM's generation process, unintentionally influencing the outcomes. While LLMs can extract planning knowledge effectively, caution must be exercised to avoid overreliance on the LLM's capabilities alone.

In conclusion, LLMs do possess valuable skills in idea generation and potential solution generation for planning and reasoning tasks. However, autonomous reasoning and planning capabilities are still a challenge for LLMs. To leverage LLMs effectively in planning tasks, it is crucial to incorporate external solvers or expert humans in the loop to verify and refine the LLM's generated solutions. By acknowledging the strengths and limitations of LLMs, we can maximize their potential in the realm of planning and reasoning.

Actionable Advice:

1. When utilizing LLMs for planning tasks, always incorporate external solvers or expert humans in the loop to verify and refine the generated solutions.
2. Avoid ascribing autonomous reasoning capabilities to LLMs and recognize their primary role in idea generation and potential solution generation.
3. Experiment with different approaches, such as incorporating model-based plan verifiers, to ensure the accuracy and correctness of the final solutions generated by LLMs.