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HumanEval

Papers

Showing 151175 of 264 papers

TitleStatusHype
A Survey on Large Language Models for Code GenerationCode2
Divide-and-Conquer Meets Consensus: Unleashing the Power of Functions in Code Generation0
SpecDec++: Boosting Speculative Decoding via Adaptive Candidate Lengths0
Qiskit Code Assistant: Training LLMs for generating Quantum Computing Code0
Kotlin ML Pack: Technical Report0
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
Can Github issues be solved with Tree Of Thoughts?Code0
Multiple-Choice Questions are Efficient and Robust LLM EvaluatorsCode1
MHPP: Exploring the Capabilities and Limitations of Language Models Beyond Basic Code GenerationCode1
MapCoder: Multi-Agent Code Generation for Competitive Problem SolvingCode2
RLHF Workflow: From Reward Modeling to Online RLHFCode5
NaturalCodeBench: Examining Coding Performance Mismatch on HumanEval and Natural User PromptsCode2
Better & Faster Large Language Models via Multi-token PredictionCode1
On the Limitations of Embedding Based Methods for Measuring Functional Correctness for Code Generation0
LayerSkip: Enabling Early Exit Inference and Self-Speculative DecodingCode3
BASS: Batched Attention-optimized Speculative Sampling0
XFT: Unlocking the Power of Code Instruction Tuning by Simply Merging Upcycled Mixture-of-ExpertsCode1
NExT: Teaching Large Language Models to Reason about Code Execution0
Low-Cost Language Models: Survey and Performance Evaluation on Python Code Generation0
Comments as Natural Logic Pivots: Improve Code Generation via Comment PerspectiveCode0
The RealHumanEval: Evaluating Large Language Models' Abilities to Support ProgrammersCode1
Self-Organized Agents: A LLM Multi-Agent Framework toward Ultra Large-Scale Code Generation and OptimizationCode1
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