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 gained significant attention in recent years for their impressive ability to generate ideas and potential solutions for various tasks. However, the question remains: can LLMs truly reason and plan? While there is skepticism surrounding their autonomous reasoning capabilities, it is important to recognize the valuable role LLMs can play in supporting reasoning and planning tasks.

One way to leverage the idea generation capabilities of LLMs is through the concept of "LLM-Modulo" setups. In these setups, LLMs are used in conjunction with model-based planners, external solvers, or expert humans in the loop. The key is to understand that LLMs generate potential answers that can be checked and refined by external solvers. By avoiding the attribution of autonomous reasoning capabilities to LLMs, we can harness their unique strengths effectively.

To test the effectiveness of LLMs in planning tasks, researchers have experimented with obfuscating the names of actions and objects in the planning problem. Surprisingly, when the effectiveness of approximate retrieval was reduced, the empirical performance of LLMs, such as GPT4, plummeted. This suggests that the improved performance of GPT4 may not solely come from its planning abilities. In fact, standard AI planners had no trouble with the obfuscation, further emphasizing the need for caution in ascribing planning capabilities to LLMs.

One approach to mitigate the limitations of LLMs in planning tasks is to incorporate an external model-based plan verifier. This verifier can perform back prompting and certify the correctness of the final solution. By relying on the expertise of external solvers, we can enhance the reliability and accuracy of the planning process. It is important to note that the Clever Hans effect, where the LLM generates guesses guided by human input, can still be a potential issue. Therefore, the role of human expertise in steering LLMs should be acknowledged and managed appropriately.

The application of LLMs in agent frameworks adds another dimension to the discussion. In the agent framework described in the research paper "2309.07870.pdf," an agent maintains its long-short term memory and has methods to observe the environment, act according to its current state, and update its memory. This factorization allows developers to easily customize agents with new functionalities. Interestingly, the inclusion of a "_is_human" property for an agent introduces the possibility of human interaction. When set to "True," the agent provides observations and memory information to a human user, allowing for human input in the decision-making process.

The SOP (State-Of-Progress) class within the agent framework represents the states of agents and specifies sub-tasks or sub-goals when accomplishing a task. This hierarchical structure enables effective coordination and collaboration among agents in complex tasks. By leveraging the capabilities of LLMs within this framework, agents can benefit from the extraction of planning knowledge.

In conclusion, while the autonomous reasoning and planning capabilities of LLMs may be questionable, their ability to generate ideas and potential solutions should not be overlooked. By incorporating external solvers, model-based plan verifiers, or expert humans in the loop, LLMs can be effectively utilized in solving planning and reasoning tasks. However, caution must be exercised to avoid ascribing autonomous reasoning to LLMs and to manage the influence of human input appropriately.

Actionable Advice:

1. When utilizing LLMs for planning tasks, consider incorporating external solvers or model-based plan verifiers to enhance the reliability and accuracy of the final solution.
2. Recognize the potential influence of human input in steering LLMs and ensure that human expertise is effectively utilized in the decision-making process.
3. Explore the possibilities of integrating LLMs within agent frameworks to leverage their idea generation capabilities and extract planning knowledge for complex tasks.