Can Large Language Models Reason and Plan?

Original Paper: https://arxiv.org/abs/2403.04121

By: Subbarao Kambhampati

Abstract

While humans sometimes do show the capability of correcting their own erroneous guesses with self-critiquing, there seems to be no basis for that assumption in the case of LLMs.

Summary Notes

Figure: An informal account of viewing LLM as a giant external non-veridical memory that acts as a pseudo System.

In the ever-evolving world of artificial intelligence, Large Language Models (LLMs) like GPT-3 and GPT-4 have captured the imagination of the tech community and beyond. These models, often described as enhanced n-gram models trained on vast web-scale datasets, have surprised many with their linguistic capabilities. However, as we delve deeper into their potential, a pertinent question arises: Can these models reason and plan, tasks traditionally reserved for System 2 cognitive processes?

Understanding the Core of LLMs

At their essence, LLMs operate as sophisticated pattern recognition systems. They predict the distribution of the next word in a sequence based on the previous words, a process best described as "approximate retrieval." Unlike databases that offer precise data retrieval, LLMs probabilistically generate text completions, leading to both creative outputs and occasional hallucinations. This characteristic is both a strength and a limitation, as it allows for novel text generation but challenges the model's ability to perform principled reasoning or planning.

Investigating Planning and Reasoning

The intrigue surrounding LLMs' capabilities extends to their potential in planning and reasoning tasks. Historically, these tasks demand a blend of domain knowledge and the ability to synthesize this knowledge into actionable plans, a process involving complex inference and search. Given their training, LLMs are not inherently equipped to perform such tasks with guaranteed correctness.

Research led by Subbarao Kambhampati at Arizona State University sought to evaluate the planning abilities of LLMs, specifically through the lens of classic planning problems like the Blocks World. Initial studies revealed that while there was some improvement in the accuracy of generated plans from GPT-3 to GPT-4, the results were far from conclusive. For instance, GPT-4 achieved only 30% empirical accuracy in the Blocks World, a figure that raises questions about its true planning capabilities.

The Role of Fine-Tuning and Prompting

Efforts to enhance LLMs' planning abilities often involve fine-tuning or carefully crafted prompting. Fine-tuning involves training the model on specific planning problems, hoping to improve its ability to generate accurate plans. However, results from such experiments have been underwhelming, suggesting that fine-tuning merely aligns LLMs' output closer to memory-based retrieval rather than genuine planning.

An alternative approach involves prompting LLMs with hints or suggestions to refine their initial guesses. This method, however, risks falling into the "Clever Hans effect," where human intervention, knowingly or unknowingly, guides the LLM towards the correct solution. This raises concerns about the true autonomy of LLMs in reasoning tasks.

Implications and Real-World Applications

Despite these challenges, LLMs hold significant potential when integrated into broader frameworks. The concept of "LLM-Modulo" frameworks suggests using LLMs as idea generators, with external verifiers ensuring the accuracy and feasibility of their outputs. This setup leverages LLMs' strengths in generating potential solutions while maintaining the rigors of sound verification.

In real-world applications, such as code generation or travel planning, LLMs can serve as valuable co-pilots, assisting human experts in brainstorming and initial drafting. However, the final validation and execution of plans remain reliant on human oversight or specialized systems.

Conclusion: A Balanced Perspective

In conclusion, while LLMs like GPT-4 exhibit remarkable capabilities in language processing, their autonomous reasoning and planning abilities are limited. They excel as tools for approximate retrieval and idea generation but require integration with sound verification systems for tasks demanding precision and accuracy.

As we continue to explore the potential of LLMs, it is crucial to recognize their current limitations and strengths. By doing so, we can better harness their capabilities in conjunction with human expertise and technological advancements, paving the way for more robust and reliable AI applications.