Large language models (LLMs) have become one of the most fascinating and controversial advancements in artificial intelligence. These models, such as GPT-4, have the ability to generate text that is indistinguishable from human-written content. But can they truly reason and plan?

Large language models (LLMs) have become one of the most fascinating and controversial advancements in artificial intelligence. These models, such as GPT-4, have the ability to generate text that is indistinguishable from human-written content. But can they truly reason and plan?

To explore this question, researchers conducted a series of experiments with GPT-4. One of the challenges they gave the model was to draw a unicorn by providing it with code. Surprisingly, GPT-4 was able to complete the task accurately. However, the researchers wanted to test whether the model had truly understood the code or if it was simply regurgitating information from its training data.

To do this, they modified the code by removing the unicorn's horn and rearranging its body parts. They then asked GPT-4 to put the horn back in the correct position. To their amazement, GPT-4 successfully completed the task, indicating that it had some level of understanding and reasoning ability.

But how exactly do LLMs reason and plan? Feed-forward networks play a crucial role in this process. These networks use vector math to reason and make predictions. The attention and feed-forward layers in LLMs have distinct functions. Attention heads retrieve information from earlier words in a prompt, while feed-forward layers enable the models to "remember" information that is not explicitly mentioned in the prompt.

Despite these impressive capabilities, there are limitations to LLMs' reasoning and planning abilities. While they excel in generating ideas and potential solutions, there are no guarantees about the accuracy or validity of these suggestions. This is where external solvers, model-based planners, or human experts come into play. LLMs can generate potential answers to be checked and refined by these external sources, but they should not be solely relied upon for autonomous reasoning.

In fact, when the researchers obfuscated the names of actions and objects in planning problems, GPT-4's performance significantly declined. This indicates that the model heavily relies on approximate retrieval and struggles with unfamiliar or obfuscated information. However, when an external model-based plan verifier was introduced to certify the correctness of the final solution, GPT-4's performance improved.

It is important to note that LLMs are not infallible. They can fall victim to what is known as the Clever Hans effect. This effect occurs when the LLM generates guesses, and it is the human in the loop who guides the model towards the correct solution, even if unintentionally. LLMs should be seen as tools that can extract planning knowledge, but their outputs should always be verified and refined by external sources.

Based on these insights, here are three actionable pieces of advice when working with LLMs for reasoning and planning tasks:

1. Leverage LLMs' idea generation capabilities: LLMs are excellent at generating ideas and potential solutions. Use them in conjunction with external solvers or human experts to explore different possibilities and expand the solution space.

2. Verify and refine LLM outputs: Never solely rely on LLM-generated solutions. Incorporate external model-based plan verifiers or expert humans to certify the correctness and validity of the final solution. This helps mitigate the Clever Hans effect and ensures accurate results.

3. Be mindful of LLM limitations: LLMs have limitations when it comes to reasoning with unfamiliar or obfuscated information. Avoid relying on approximate retrieval alone and provide clear and unambiguous prompts to improve performance.

In conclusion, while LLMs have shown impressive reasoning and planning abilities, it is important to recognize their limitations and approach their outputs with caution. By leveraging their idea generation capabilities and incorporating external verifiers, LLMs can be valuable tools in solving planning and reasoning tasks. However, autonomous reasoning should not be attributed solely to LLMs, and their outputs should always be verified and refined.