LLMs Can Plan Only If We Tell Them (2501.13545v1)

Abstract: LLMs have demonstrated significant capabilities in natural language processing and reasoning, yet their effectiveness in autonomous planning has been under debate. While existing studies have utilized LLMs with external feedback mechanisms or in controlled environments for planning, these approaches often involve substantial computational and development resources due to the requirement for careful design and iterative backprompting. Moreover, even the most advanced LLMs like GPT-4 struggle to match human performance on standard planning benchmarks, such as the Blocksworld, without additional support. This paper investigates whether LLMs can independently generate long-horizon plans that rival human baselines. Our novel enhancements to Algorithm-of-Thoughts (AoT), which we dub AoT+, help achieve state-of-the-art results in planning benchmarks out-competing prior methods and human baselines all autonomously.

A Critical Evaluation of "LLMs Can Plan Only If We Tell Them"

The paper "LLMs Can Plan Only If We Tell Them" provides an analytical exploration into the planning capabilities of LLMs, focusing on their limitations and potential enhancements. It explores the intrinsic shortcomings of LLMs in autonomous planning tasks and introduces a novel approach, AoT+ (Algorithm-of-Thoughts Plus), to bolster these capabilities.

The Challenges of Planning with LLMs

The paper begins by acknowledging the profound improvements LLMs have made in natural language processing and reasoning domains, attributed to their underlying transformer architecture. Despite these advancements, the paper posits that LLMs exhibit significant deficiencies in autonomous planning. This inadequacy becomes evident when LLMs are tasked with long-horizon planning, necessitating them to independently generate sequences of actions to achieve complex goals.

The authors identify a fundamental issue: LLMs struggle with self-verification and inductive reasoning, limiting their ability to backtrack errors or recognize successful completions in planning environments. Consequently, their performance on standard planning benchmarks such as Blocksworld remains subpar, falling significantly behind human-level proficiency without supplementary assistance through external tools or hybrid methods.

Innovations in AoT+

AoT+, an enhancement over the existing Algorithm-of-Thoughts (AoT) framework, is proposed as a promising solution to the highlighted limitations of LLMs in planning. The paper provides a detailed breakdown of the methodological innovations incorporated in AoT+, which include:

1. Periodic Structured State Generation: This involves regenerating and restating the current problem state periodically to alleviate cognitive load on LLMs, thereby reducing state hallucination and improving accuracy in plan execution.
2. Random Trajectory Augmentation: Here, the process involves generating search trajectories using a combination of successful and contrived paths. This augmentation increases robustness and helps in generalizing across various planning scenarios without heavily relying on pre-defined heuristics.

AoT+ integrates these methods to activate what the authors term "System 3 thinking"—a deliberate and analytical mode of decision-making akin to human problem-solving under uncertainty.

Empirical Validation

The paper presents empirical evidence from several benchmark tests to substantiate the efficacy of AoT+. These include Blocksworld and Logistics for planning and List Functions and ACRE for inductive reasoning. The results demonstrate that AoT+ not only outperforms previous LLM approaches but achieves results superseding human baselines in certain cases.

Notably, AoT+ exhibits superiority over methodologies requiring external feedback mechanisms, such as LLM-Modulo frameworks, without incurring the computational overhead associated with iterative backprompting. This not only underscores the potential of AoT+ in enhancing computational efficiency but also its adaptability across different LLM architectures.

Theoretical and Practical Implications

The research contributes to our understanding of how LLMs can be more effectively utilized in domains that necessitate complex planning and sequential decision-making. By highlighting the latent capabilities of LLMs that can be activated through advanced prompting techniques, this paper calls for a paradigm shift in how planning tasks are approached using AI.

Practically, the findings have wide-ranging implications for AI applications in logistics, robotics, and any field requiring dynamic plan adjustment. Theoretically, it proposes a novel perspective on the cognitive parallels between human reasoning and machine learning, suggesting areas for further exploration in LLM cognitive capabilities.

Future Directions

The paper concludes with a reflection on the potential paths for future research, particularly in refining state-tracking mechanisms and enhancing heuristic discovery in LLMs. The authors argue for continued exploration into the cognitive processes of LLMs, aiming for a holistic understanding that bridges AI planning to human-like decision-making.

In essence, "LLMs Can Plan Only If We Tell Them" provides a comprehensive investigation into the planning deficiencies of LLMs and offers a compelling argument for how these limitations might be overcome. The introduction of AoT+ represents a significant step forward in realizing fully autonomous planning within AI frameworks.