Can LLMs Really Reason, Plan, and Provide Value?

Can LLMs Really Reason, Plan, and Provide Value?

When it comes to LLMs (Large Language Models), there has been much debate about their capabilities in reasoning and planning. While some argue that LLMs excel in idea generation for various tasks, others question whether they possess true planning and reasoning abilities. In this article, we will explore these viewpoints and discuss how LLMs can still play a constructive role in solving planning and reasoning tasks.

It is important to note that while LLMs may not have autonomous reasoning capabilities, their ability to generate ideas and potential solutions should not be overlooked. This can be particularly valuable in what is known as "LLM-Modulo" setups. In these setups, LLMs can work in conjunction with model-based planners, external solvers, or expert humans in the loop. The key is to view LLMs as generators of potential answers that require external verification and refinement, rather than autonomous reasoners.

To further understand the role of LLMs in planning tasks, researchers have conducted experiments with GPT4, a highly advanced LLM. One approach involved obfuscating the names of actions and objects in planning problems to reduce the effectiveness of approximate retrieval. Surprisingly, GPT4's performance significantly declined in these obfuscated domains, despite standard AI planners having no trouble with such obfuscation. In the Blocks World domain, GPT4 only achieved 30% empirical accuracy, highlighting the limitations of its planning abilities.

To address this issue, researchers have proposed using external model-based plan verifiers to certify the correctness of the final solution. This approach allows the LLM to generate potential solutions while relying on an external verifier for validation. By leveraging the strengths of both LLMs and external solvers, the accuracy and reliability of the planning process can be improved.

However, it is essential to be cautious of the Clever Hans effect when utilizing LLMs in planning tasks. The Clever Hans effect refers to the phenomenon where the LLM is merely generating guesses, while it is the human in the loop who guides the LLM towards the correct solution. This unintentional steering by humans can lead to misleading results, as the LLM may not possess true reasoning capabilities. Therefore, it is crucial to distinguish between genuine reasoning and the extraction of planning knowledge by LLMs.

In addition to the debates surrounding LLMs' reasoning and planning abilities, there are considerations regarding efficiency and quality. In the training process, in-batch negatives allow LLMs to reuse representations computed in the same training batch. This approach proves to be more efficient compared to calculating representations for extra negatives. By keeping the datastore throughout training and updating only the vector representations, higher quality representations can be achieved over time. This not only improves efficiency but also reduces the occurrence of hallucination, where the LLM generates misleading or inaccurate information.

In conclusion, while the capabilities of LLMs in reasoning and planning may be questioned, they still have valuable roles to play in planning and reasoning tasks. By leveraging their idea generation abilities in conjunction with external solvers or human experts, the accuracy and effectiveness of planning can be enhanced. However, it is crucial to recognize the limitations of LLMs and avoid ascribing autonomous reasoning capabilities to them. Additionally, employing external plan verifiers and being cautious of the Clever Hans effect can ensure the reliability of the final solutions generated by LLMs.