LeetCode
Emerging42papers using it
2022first seen
LeetCode is a dataset containing 944 real-world coding problems across five programming languages, used to evaluate the performance of large language models in code generation through various metrics.
Papers using LeetCode (42)
- AI-Powered, But Power-Hungry? Energy Efficiency of LLM-Generated CodePiloting Copilot, Codex, and StarCoder2: Hot Temperature, Cold Prompts, or Black Magic?Comparing large language models and human programmers for generating
programming codeEffiBench: Benchmarking the Efficiency of Automatically Generated CodeEnergy-Aware Code Generation with LLMs: Benchmarking Small vs. Large Language Models for Sustainable AI ProgrammingTESTEVAL: Benchmarking Large Language Models for Test Case GenerationEnhancing LLM-Based Code Generation with Complexity Metrics: A Feedback-Driven ApproachDo Comments and Expertise Still Matter? An Experiment on Programmers' Adoption of AI-Generated JavaScript CodeAnalyzing Prominent LLMs: An Empirical Study of Performance and Complexity in Solving LeetCode ProblemsChain-of-Code Collapse: Reasoning Failures in LLMs via Adversarial Prompting in Code GenerationA case study on the transformative potential of AI in software
engineering on LeetCode and ChatGPTBeyond Pass Rate: A Multilingual, Execution-Grounded Evaluation of Open Code LLMsVeriContest: A Competitive-Programming Benchmark for Verifiable Code GenerationThe Readability Spectrum: Patterns, Issues, and Prompt Effects in LLM-Generated CodeThink Anywhere in Code GenerationHolistic Evaluation of State-of-the-Art LLMs for Code GenerationDRIVE: Data Curation Best Practices for Reinforcement Learning with Verifiable Reward in Competitive Code GenerationDRIVE: Data Curation Best Practices for Reinforcement Learning with
Verifiable Reward in Competitive Code GenerationToward Green Code: Prompting Small Language Models for Energy-Efficient Code GenerationSLICET5: Static Program Slicing using Language Models with Copy Mechanism and Constrained DecodingEvaluating the Energy-Efficiency of the Code Generated by LLMsLLM Performance for Code Generation on Noisy TasksPerformance Review on LLM for solving leetcode problemsAssessing AI Detectors in Identifying AI-Generated Code: Implications
for EducationDebugBench: Evaluating Debugging Capability of Large Language ModelsLiveCodeBench: Holistic and Contamination Free Evaluation of Large
Language Models for CodeOn Evaluating the Efficiency of Source Code Generated by LLMsPanGu-Coder2: Boosting Large Language Models for Code with Ranking
FeedbackEnhancing Computer Programming Education with LLMs: A Study on Effective
Prompt Engineering for Python Code GenerationAutomated Repair of Programs from Large Language ModelsGitHub Copilot: the perfect Code compLeeter?Think Outside the Code: Brainstorming Boosts Large Language Models in
Code GenerationAre Large Language Models a Threat to Programming Platforms? An
Exploratory StudyLeveraging Print Debugging to Improve Code Generation in Large Language
ModelsPython Symbolic Execution with LLM-powered Code GenerationEvaluating the Quality of Code Comments Generated by Large Language
Models for Novice ProgrammersArtificial-Intelligence Generated Code Considered Harmful: A Road Map
for Secure and High-Quality Code GenerationBenchmarking ChatGPT, Codeium, and GitHub Copilot: A Comparative Study
of AI-Driven Programming and Debugging AssistantsCan OpenSource beat ChatGPT? -- A Comparative Study of Large Language
Models for Text-to-Code GenerationProgram Slicing in the Era of Large Language ModelsEvaluating ChatGPT-3.5 Efficiency in Solving Coding Problems of
Different Complexity Levels: An Empirical AnalysisA Performance Study of LLM-Generated Code on Leetcode