Can Large Language Models Reason and Plan?

Core Concepts

Large Language Models (LLMs) excel in universal approximate retrieval but lack principled reasoning abilities, despite claims suggesting otherwise.

Abstract

Large Language Models (LLMs) are powerful tools trained on vast language corpora, offering approximate retrieval capabilities rather than principled reasoning. The author questions the planning and reasoning abilities of LLMs, highlighting the challenges in distinguishing between memorization and problem-solving. While LLMs show promise in idea generation, their limitations in autonomous planning are evident through empirical testing results.

Stats

GPT4 reached 30% empirical accuracy in the Blocks World.
LLMs can support approximate retrieval over web-scale corpora.
GPT4's performance plummeted when obfuscating names of actions and objects in planning problems.
Fine-tuning LLMs on planning problems converts tasks into memory-based approximate retrieval.
Self-verification by LLMs worsens performance due to hallucinations of false positives and negatives.

Quotes

"LLMs do excel in idea generation for any task–including those involving reasoning." - Subbarao Kambhampati
"Nothing that I have read, verified, or done gives me any compelling reason to believe that LLMs do reasoning/planning." - Subbarao Kambhampati

Key Insights Distilled From

Can Large Language Models Reason and Plan?

by Subbarao Kam... at arxiv.org 03-08-2024

https://arxiv.org/pdf/2403.04121.pdf

Deeper Inquiries

How can leveraging LLMs' idea generation capabilities benefit planning tasks without ascribing autonomous reasoning to them?

In the context of Large Language Models (LLMs), their exceptional ability to generate ideas and potential solutions can be harnessed effectively in planning tasks without attributing autonomous reasoning capabilities to them. By utilizing LLMs for idea generation, planners can leverage the vast amount of knowledge stored in these models to propose various approaches or strategies for solving complex problems. These generated ideas serve as valuable starting points for human experts or model-based verifiers to evaluate and refine into executable plans.
One key advantage of using LLMs for idea generation is that it allows planners to explore a wide range of possible solutions quickly. This rapid ideation process can help in brainstorming creative alternatives, identifying potential pitfalls, and considering different perspectives on how a task could be accomplished. Additionally, by involving external verifiers or expert humans in the loop to validate and enhance these generated ideas, planners can ensure that the final plans are well-informed and robust.
Overall, leveraging LLMs' idea generation capabilities provides a valuable resource for planning tasks by offering diverse suggestions and insights while maintaining oversight from external sources to verify and improve upon these initial concepts.

How does reliance on human prompting impact the claimed planning abilities of LLMs?

The dependence on human prompting significantly influences the purported planning abilities attributed to Large Language Models (LLMs). When humans are involved in guiding or steering LLM-generated plans through iterative prompts, there is a risk of introducing bias or inadvertently influencing the outcomes. In such scenarios, where humans provide corrections or adjustments based on their understanding of correct solutions, it becomes challenging to ascertain whether the success of the plan stems from genuine reasoning within the LLM itself or merely reflects guided guesswork.
Furthermore, continuous human intervention during plan generation may lead to what is known as the Clever Hans effect—a phenomenon where an entity appears intelligent due to subtle cues provided by observers rather than intrinsic intelligence. In this context, if humans play a significant role in refining LLM-generated plans without full comprehension or verification capability themselves, it raises questions about the true autonomy and reasoning capacity of these models.
Therefore, when evaluating claims about LLMs' planning abilities influenced by human prompting, it is essential to consider how much credit should be attributed solely...

How can using LLM-Modulo frameworks address challenges posed by limitations in principled reasoning exhibited by LMMs?

The adoption of LMM-Modulo frameworks offers a structured approach towards mitigating challenges associated with Large Language Models' (LLMs) constraints in principled reasoning while still capitalizing on their strengths. By integrating external model-based plan verifiers within this framework...