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Benchmarking ChatGPT on Algorithmic Reasoning (2404.03441v2)

Published 4 Apr 2024 in cs.AI, cs.CL, and cs.LG

Abstract: We evaluate ChatGPT's ability to solve algorithm problems from the CLRS benchmark suite that is designed for GNNs. The benchmark requires the use of a specified classical algorithm to solve a given problem. We find that ChatGPT outperforms specialist GNN models, using Python to successfully solve these problems. This raises new points in the discussion about learning algorithms with neural networks and how we think about what out of distribution testing looks like with web scale training data.

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Citations (3)
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Summary

  • The paper demonstrates that ChatGPT significantly outperforms specialized GNN models across a range of algorithmic tasks in the CLRS benchmark suite.
  • It employs prompt engineering and natural language cues to transform algorithmic problems into Python code solutions effectively.
  • The study identifies limitations in dynamic programming tasks and recommends iterative prompting and infrastructural enhancements for future improvements.

Benchmarking ChatGPT on Algorithmic Reasoning

Introduction

The paper explores the capabilities of ChatGPT in solving algorithmic problems derived from the CLRS benchmark suite, traditionally aimed at evaluating Graph Neural Networks (GNNs). This benchmark suite encompasses a wide variety of algorithmic concepts, including sorting, searching, dynamic programming, and graph algorithms, among others. The authors position their work in the context of the recent advancements in neural algorithm synthesis, particularly highlighting the shift towards large generalist models like GPT-4 over specialized systems. Their investigation into ChatGPT's performance on these classical algorithm problems, typically dominated by GNN models, reveals compelling insights into the potential of leveraging LLMs for algorithmic reasoning and problem-solving.

Benchmark Performance

The performance of ChatGPT on the CLRS benchmark suite reveals several interesting findings:

  • General Overview: ChatGPT significantly outperformed specialist GNN models across most tasks in the suite, showcasing its ability to understand and generate Python code to solve problems effectively.
  • Task Categories: The tasks spanned eight categories including sorting, searching, divide and conquer, greedy algorithms, dynamic programming, graph algorithms, string matching, and geometry problems.
  • Comparative Analysis: Comparison with state-of-the-art GNN models, specifically those developed in recent studies, illustrates that ChatGPT is competitive, often surpassing these models in over two thirds of the tasks.
  • Dynamic Programming: Despite its overall impressive performance, ChatGPT shows relative struggles with dynamic programming tasks, indicating possible avenues for future enhancements or different prompting strategies.

Methodology and Dataset Adaptation

A notable aspect of this research is the adaptation of algorithmic problems for evaluation by ChatGPT. This involved:

  • Prompt Engineering: The problems were presented to ChatGPT in natural language along with minimal descriptions of the desired algorithmic outcomes, showcasing the model's ability to interpret and solve complex problems without explicit programming instructions.
  • Dataset Handling: For problems with large input sizes exceeding the context window of ChatGPT, auxiliary methods such as file uploads were employed, hinting at interesting challenges and solutions in handling large datasets with LLMs.
  • Evaluation Criteria: The evaluation extended beyond simply generating correct outputs, examining ChatGPT's adherence to algorithmic processes and correctness in intermediate steps, which aligns with the benchmark's goal of evaluating algorithmic reasoning.

Limitations and Future Directions

The paper also acknowledges several limitations and areas for future work:

  • Challenge with Dynamic Programming: The noted challenges with dynamic programming tasks prompt further investigation into model prompting strategies or deeper insights into model limitations.
  • Infrastructural Constraints: The reliance on Beta features such as code execution in ChatGPT leads to occasional errors, highlighting the importance of stable infrastructural support for comprehensive evaluation.
  • Potential for Iterative Improvement: Exploring iterative prompting or feedback loops was suggested as a potential avenue to enhance model performance further, alongside leveraging LLMs for more transparent decision-making processes.

Implications and Conclusions

This paper's findings emphasize the evolving landscape of algorithmic problem-solving, where generalist LLMs like ChatGPT demonstrate remarkable capabilities, challenging the prevailing dominance of specialized models. It highlights the potential of these models in understanding complex problem statements and executing algorithmic solutions effectively, marking a significant stride in the field of neural algorithm synthesis.

Furthermore, the paper speculates on the broader implications of employing LLMs for algorithmic reasoning, suggesting a future where these models could complement or even discover novel algorithmic solutions, thereby expanding our toolkit for tackling computational problems. However, it also underscores the ongoing need for specialized models, especially in contexts where computational efficiency and resource constraints are paramount.

In conclusion, the exploration of ChatGPT's performance on the CLRS benchmark suite offers insightful perspectives on the capabilities and potential of LLMs in algorithmic reasoning. It sets the stage for future research directions, including refined prompting strategies, the exploration of iterative feedback mechanisms, and the development of hybrid models that leverage both generalist and specialist strengths.

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