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Showing 51–75 of 129 papers

TitleStatusHype
Rethinking Repetition Problems of LLMs in Code GenerationCode1
RLTF: Reinforcement Learning from Unit Test FeedbackCode1
EffiLearner: Enhancing Efficiency of Generated Code via Self-OptimizationCode1
Unchosen Experts Can Contribute Too: Unleashing MoE Models' Power by Self-ContrastCode1
Unsupervised Evaluation of Code LLMs with Round-Trip CorrectnessCode1
XFT: Unlocking the Power of Code Instruction Tuning by Simply Merging Upcycled Mixture-of-ExpertsCode1
Discrete Flow Matching—0
DSTC: Direct Preference Learning with Only Self-Generated Tests and Code to Improve Code LMs—0
DynaCode: A Dynamic Complexity-Aware Code Benchmark for Evaluating Large Language Models in Code Generation—0
Structured Chain-of-Thought Prompting for Code Generation—0
Enhancing LLM-Based Code Generation with Complexity Metrics: A Feedback-Driven Approach—0
Enhancing Reasoning Capabilities of Small Language Models with Blueprints and Prompt Template Search—0
Evaluating LLM-driven User-Intent Formalization for Verification-Aware Languages—0
Selection of Prompt Engineering Techniques for Code Generation through Predicting Code Complexity—0
Grammar-Based Code Representation: Is It a Worthy Pursuit for LLMs?—0
Guideline Forest: Experience-Induced Multi-Guideline Reasoning with Stepwise Aggregation—0
Self-Explained Keywords Empower Large Language Models for Code Generation—0
What I cannot execute, I do not understand: Training and Evaluating LLMs on Program Execution Traces—0
Interactive Code Generation via Test-Driven User-Intent Formalization—0
Code-Optimise: Self-Generated Preference Data for Correctness and Efficiency—0
Interval-censored Hawkes processes—0
Synthesize, Partition, then Adapt: Eliciting Diverse Samples from Foundation Models—0
Isolating Language-Coding from Problem-Solving: Benchmarking LLMs with PseudoEval—0
CodeMixBench: Evaluating Large Language Models on Code Generation with Code-Mixed Prompts—0
Large Language Model-Aware In-Context Learning for Code Generation—0
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