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 garnered significant attention in recent years for their impressive ability to generate ideas and potential solutions for various tasks. However, there is a lingering question regarding the extent to which LLMs can truly reason and plan. While some argue that LLMs lack autonomous reasoning capabilities, it is important to recognize their value in supporting reasoning and planning tasks.

One of the key strengths of LLMs lies in their capacity for idea generation. They excel at producing potential candidate solutions, although the reliability of these guesses may vary. This unique ability can be effectively leveraged in what we call "LLM-Modulo" setups. In these setups, LLMs work in conjunction with model-based planners, external solvers, or expert humans in the loop. The role of LLMs is to generate ideas that can be checked and refined by external entities, rather than ascribing autonomous reasoning capabilities to them. Frameworks like LangChain exemplify this approach.

To assess the planning capabilities of LLMs, researchers have experimented with obfuscating the names of actions and objects in planning problems. This reduces the effectiveness of approximate retrieval and allows for a more accurate evaluation of LLM performance. Surprisingly, when subjected to obfuscation, LLMs like GPT4 experience a significant decline in empirical performance. While standard AI planners handle such obfuscation without difficulty, LLMs struggle to maintain their accuracy, demonstrating their limitations in planning tasks.

One possible solution to enhance the reliability of LLM-generated solutions is to incorporate external model-based plan verifiers. These verifiers can assess the correctness of the final solution, providing a more robust approach. By leveraging the strengths of both LLMs and external verifiers, we can mitigate the risks associated with relying solely on LLM-generated plans.

However, it is crucial to acknowledge the potential pitfalls of using LLMs in planning tasks. The Clever Hans effect, where LLMs generate guesses that are steered by human knowledge, can undermine the authenticity of LLM-generated plans. Humans in the loop often unintentionally guide LLMs by possessing knowledge of right and wrong solutions. This phenomenon highlights the need for careful oversight and verification when utilizing LLMs for planning purposes.

Despite these limitations, the fact that LLMs are adept at extracting planning knowledge should not be overlooked. This knowledge can still be harnessed effectively in combination with external solvers or human expertise. By understanding the role of LLMs as idea generators rather than autonomous planners, we can make the most of their capabilities while mitigating their shortcomings.

In conclusion, while LLMs may not possess inherent reasoning and planning capabilities, they offer valuable contributions in the realm of idea generation. By incorporating external verifiers, leveraging LLM-Modulo setups, and being mindful of the Clever Hans effect, we can maximize the potential of LLMs in planning and reasoning tasks. As the field continues to advance, it is crucial to explore new avenues for collaboration between LLMs, external entities, and human experts to unlock the true potential of language models in problem-solving.