Can LLMs Really Reason and Plan? Exploring the Potential and Limitations

Can LLMs Really Reason and Plan? Exploring the Potential and Limitations

The capabilities of Language Models with Large Memory (LLMs) have been a topic of great interest and debate in the field of artificial intelligence. One question that has been raised is whether LLMs can truly reason and plan, or if their abilities are limited to idea generation. While it is true that LLMs excel in generating potential solutions for various tasks, including those involving reasoning, it is important to understand how these generated ideas can be effectively leveraged to support planning and reasoning.

It is worth noting that while LLMs may not possess autonomous reasoning capabilities, they can still play a constructive role in solving planning and reasoning tasks. In what is known as "LLM-Modulo" setups, LLMs can generate ideas and potential candidate solutions, which can then be checked and refined by external solvers or expert humans in the loop. This recognizes that LLMs are not autonomously reasoning, but rather generating potential answers to be further validated.

One popular framework that operates on this principle is LangChain, which utilizes LLMs in conjunction with external solvers or human experts. By understanding the role of LLMs as idea generators rather than autonomous reasoners, their uncanny ability to generate potential solutions can be effectively harnessed.

To further investigate the capabilities of LLMs in planning tasks, researchers have conducted experiments with GPT4, a state-of-the-art language model. One approach involved obfuscating the names of actions and objects in planning problems to reduce the effectiveness of approximate retrieval. Surprisingly, GPT4's empirical performance plummeted in these obfuscated domains, despite the fact that standard AI planners had no trouble with the same obfuscation.

This suggests that the improved performance of GPT4 may not solely be attributed to its ability to plan, but rather to its proficiency in approximate retrieval. In fact, GPT4 achieved only 30% empirical accuracy in the Blocks World domain, indicating that its planning capabilities may be limited.

To overcome this limitation, a promising approach is to incorporate an external model-based plan verifier. By letting the verifier do the back prompting and certify the correctness of the final solution, the reliance on the LLM's autonomous reasoning abilities is reduced. This approach ensures that the final solution is thoroughly validated, mitigating the risk of erroneous outputs.

However, it is important to be cautious when using CoT (Clever Hans effect) in conjunction with LLMs. CoT involves the human in the loop steering the LLM based on their knowledge of right and wrong solutions. This can unintentionally influence the LLM's generated guesses, leading to potentially biased or incorrect results. Therefore, it is crucial to ensure that LLMs are not solely relied upon for reasoning and planning tasks, but rather used in conjunction with external verification processes.

In the realm of generative agents, the concept of interactive simulacra of human behavior has gained significant attention. The ability of these agents to simulate human-like behavior opens up possibilities for various applications. In an effort to create such generative agents, researchers have developed a retrieval function that scores memories based on recency, relevance, and importance.

By normalizing these scores, a weighted combination is calculated to determine the overall retrieval score. This retrieval function enables the selection of memories that fit within the language model's context window. Additionally, reflections, which are higher-level and more abstract thoughts generated by the agent, are periodically generated based on the importance scores of recent events.

To utilize these generative agents effectively, it is important to ask salient high-level questions about the subjects in the statements. These questions can serve as queries for retrieval, gathering relevant memories and reflections. This approach allows for a more comprehensive understanding of the agents' experiences and thought processes.

When it comes to planning the agenda for a day, a top-down approach can be adopted. By prompting the language model with the agent's summary description and a summary of their previous day, an initial plan outlining the day's agenda in broad strokes can be created. This top-down approach can then be recursively expanded to generate more detailed plans.

In conclusion, while LLMs may not possess autonomous reasoning and planning capabilities, their ability to generate potential solutions can still be valuable when used in conjunction with external validation processes. By understanding the limitations and leveraging the strengths of LLMs, researchers and practitioners can harness their potential effectively. However, it is important to be cautious of biases and unintended influences in the LLM's outputs, ensuring that the human in the loop plays a critical role in steering and validating the generated solutions.

Actionable advice:

1. When utilizing LLMs for planning and reasoning tasks, incorporate an external model-based plan verifier to ensure the correctness of the final solution.
2. Be cautious when using CoT (Clever Hans effect) with LLMs, as it can unintentionally steer the LLM and lead to biased or incorrect results. Use external verification processes to mitigate this risk.
3. When working with generative agents, ask salient high-level questions to gather relevant memories and reflections, enabling a more comprehensive understanding of the agents' experiences and thought processes.