mlsanityData to 14 Oct 2026

Performance Review on LLM for solving leetcode problems

arXiv 2502.15770 ↗First posted 16 Feb 2025cs.SE cs.AIcited by 5

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Abstract (arXiv)

This paper presents a comprehensive performance evaluation of Large Language Models (LLMs) in solving programming challenges from Leetcode, a widely used platform for algorithm practice and technical interviews. We began by crawling the Leetcode website to collect a diverse set of problems encompassing various difficulty levels and topics. Using this dataset, we generated solutions with multiple LLMs, including GPT-4 and GPT-3.5-turbo (ChatGPT-turbo). The generated solutions were systematically evaluated for correctness and efficiency. We employed the pass@k metric to assess the success rates within a given number of attempts and analyzed the runtime performance of the solutions. Our results highlight the strengths and limitations of current LLMs [10] in code generation and problem-solving tasks, providing insights into their potential applications and areas for improvement in automated programming assistance.

Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). Builds on 6 of its 17 references that are in the map. First posted on arXiv 16 Feb 2025.

Builds on

6 of 17 references.

References · 6

  1. 2024A Performance Study of LLM-Generated Code on Leetcodecited by 49
  2. 2019The FacT: Taming Latent Factor Models for Explainability with Factorization Treescited by 27
  3. 2024Deep Reinforcement Learning-based Obstacle Avoidance for Robot Movement in Warehouse Environmentscited by 17
  4. 2024NEVLP: Noise-Robust Framework for Efficient Vision-Language Pre-trainingcited by 15
  5. 2024Graphical Structural Learning of rs-fMRI data in Heavy Smokerscited by 11
  6. 2024Few-Shot Learning with Adaptive Weight Masking in Conditional GANscited by 4

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 4; 2026: 1.

5 papers.

  1. 2025Evaluating Supervised Learning Models for Fraud Detection: A Comparative Study of Classical and Deep Architectures on Imbalanced Transaction Datacited by 20
  2. 2025Reward-Shifted Speculative Sampling Is An Efficient Test-Time Weak-to-Strong Alignercited by 4
  3. 2025How Do Agentic AI Systems Address Performance Optimizations? A BERTopic-Based Analysis of Pull Requestscited by 1
  4. 2026On the Stability of Prompt Ranking in Large Language Model Evaluationcited by 1
  5. 2025MaCP: Minimal yet Mighty Adaptation via Hierarchical Cosine Projectioncited by 0