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Can Language Models Solve Olympiad Programming? (2404.10952v1)

Published 16 Apr 2024 in cs.CL, cs.AI, and cs.PL

Abstract: Computing olympiads contain some of the most challenging problems for humans, requiring complex algorithmic reasoning, puzzle solving, in addition to generating efficient code. However, it has been understudied as a domain to evaluate LLMs (LMs). In this paper, we introduce the USACO benchmark with 307 problems from the USA Computing Olympiad, along with high-quality unit tests, reference code, and official analyses for each problem. These resources enable us to construct and test a range of LM inference methods for competitive programming for the first time. We find GPT-4 only achieves a 8.7% pass@1 accuracy with zero-shot chain-of-thought prompting, and our best inference method improves it to 20.2% using a combination of self-reflection and retrieval over episodic knowledge. However, this is far from solving the benchmark. To better understand the remaining challenges, we design a novel human-in-the-loop study and surprisingly find that a small number of targeted hints enable GPT-4 to solve 13 out of 15 problems previously unsolvable by any model and method. Our benchmark, baseline methods, quantitative results, and qualitative analysis serve as an initial step toward LMs with grounded, creative, and algorithmic reasoning.

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Authors (4)
  1. Quan Shi (26 papers)
  2. Michael Tang (14 papers)
  3. Karthik Narasimhan (82 papers)
  4. Shunyu Yao (72 papers)
Citations (9)
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Summary

Evaluating LLMs on USACO Benchmark: New Insights into Competitive Programming

Introduction to USACO Benchmark

The paper introduces the USACO benchmark, a set of 307 challenging problems from the USA Computing Olympiad. These problems are designed to rigorously assess LMs in the domain of competitive programming, pushing beyond the capabilities required by simpler coding benchmarks like HumanEval, which have solve rates above 90%. Critical to the USACO benchmark's design is the inclusion of high-quality unit tests, reference codes, and official problem analyses for each problem, which positions it uniquely for comprehensive evaluation of algorithmic reasoning in LMs.

Key Findings and Methodological Innovations

Baseline Model Performance

The baseline performance of GPT-4 using zero-shot chain-of-thought prompting on the USACO benchmark stands notably low at 8.7% pass@1 rate. This low performance underscores the benchmark's complexity and the limitations of current LMs when faced with tasks requiring deep, creative algorithmic thinking.

Enhanced Inference Methods

Advanced inference methodologies significantly improve model performance. The most effective mechanism combines retrieval over episodic knowledge and self-reflection, elevating GPT-4's pass@1 rate to 20.2%. This underlines the possible directions for leveraging both domain-specific data and dynamic reasoning processes to enhance model outputs.

Theoretical and Practical Implications

Gap in Complex Problem Solving

Even with the best performing methods, LMs are unable to surpass the bronze level of the USACO benchmark, indicating a significant gap in models' capabilities in tackling higher-order computational problems that require inventive algorithm design and specialized problem-solving strategies.

Value of Episodic Retrieval and Self-Reflection

The combination of episodic retrieval and self-reflection not only extends model capabilities but also suggests that models can benefit from structures mimicking humanlike learning patterns, such as learning from past experience (episodic) and self-review mechanisms.

Human-in-the-loop Results

A novel human-in-the-loop paper reveals that GPT-4, with the help of minimal human guidance, was able to solve 13 out of 15 challenging problems that were initially unsolvable both by itself and GPT-3.5, regardless of the inference method used. This surprising finding illustrates that models might possess latent capabilities which are accessible only under specific interactive conditions, suggesting potential in collaborative human-AI problem-solving setups.

Future Research Directions

The paper strongly advocates for further exploration into:

  1. Inference techniques that could better harness the undocumented capabilities of LMs.
  2. Interactive models where LMs can use feedback more effectively, simulating a real-world learning environment.
  3. Evaluation metrics that better capture the nuances of model competencies beyond mere pass/fail rates, possibly considering the extent of correct logic or the closeness to a solution.

Conclusion

This paper makes a compelling case for the further development of competitive programming benchmarks for evaluating and advancing the state of LMs in complex problem-solving scenarios. Through detailed analyses and methodological innovations, it sets a new path for understanding and enhancing the algorithmic capacities of future LLMs.

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