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HumanEval

Papers

Showing 5175 of 264 papers

TitleStatusHype
CodeCriticBench: A Holistic Code Critique Benchmark for Large Language ModelsCode1
Code-Vision: Evaluating Multimodal LLMs Logic Understanding and Code Generation CapabilitiesCode1
Learning to Generate Unit Tests for Automated DebuggingCode1
How to Select Datapoints for Efficient Human Evaluation of NLG Models?Code1
MyGO Multiplex CoT: A Method for Self-Reflection in Large Language Models via Double Chain of Thought ThinkingCode1
HumanEval Pro and MBPP Pro: Evaluating Large Language Models on Self-invoking Code GenerationCode1
Planning-Driven Programming: A Large Language Model Programming WorkflowCode1
PerfCodeGen: Improving Performance of LLM Generated Code with Execution FeedbackCode1
Can Language Models Replace Programmers for Coding? REPOCOD Says 'Not Yet'Code1
HumanEval-V: Evaluating Visual Understanding and Reasoning Abilities of Large Multimodal Models Through Coding TasksCode1
Training Language Models on Synthetic Edit Sequences Improves Code SynthesisCode1
Policy Filtration in RLHF to Fine-Tune LLM for Code GenerationCode1
How Do Your Code LLMs Perform? Empowering Code Instruction Tuning with High-Quality DataCode1
Planning In Natural Language Improves LLM Search For Code GenerationCode1
ArchCode: Incorporating Software Requirements in Code Generation with Large Language ModelsCode1
InverseCoder: Self-improving Instruction-Tuned Code LLMs with Inverse-InstructCode1
RES-Q: Evaluating Code-Editing Large Language Model Systems at the Repository ScaleCode1
How Efficient is LLM-Generated Code? A Rigorous & High-Standard BenchmarkCode1
SemCoder: Training Code Language Models with Comprehensive Semantics ReasoningCode1
ReflectionCoder: Learning from Reflection Sequence for Enhanced One-off Code GenerationCode1
EffiLearner: Enhancing Efficiency of Generated Code via Self-OptimizationCode1
Unchosen Experts Can Contribute Too: Unleashing MoE Models' Power by Self-ContrastCode1
Instruction Tuning With Loss Over InstructionsCode1
Multiple-Choice Questions are Efficient and Robust LLM EvaluatorsCode1
MHPP: Exploring the Capabilities and Limitations of Language Models Beyond Basic Code GenerationCode1
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