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Showing 51–100 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
CodeMirage: Hallucinations in Code Generated by Large Language Models—0
Test-Driven Development for Code Generation—0
Learning to Reason via Self-Iterative Process Feedback for Small Language Models—0
Textbooks Are All You Need—0
LiveCodeBench: Holistic and Contamination Free Evaluation of Large Language Models for Code—0
LLaDA 1.5: Variance-Reduced Preference Optimization for Large Language Diffusion Models—0
Bridging the Language Gap: Enhancing Multilingual Prompt-Based Code Generation in LLMs via Zero-Shot Cross-Lingual Transfer—0
Bridging Code Semantic and LLMs: Semantic Chain-of-Thought Prompting for Code Generation—0
USCD: Improving Code Generation of LLMs by Uncertainty-Aware Selective Contrastive Decoding—0
The Program Testing Ability of Large Language Models for Code—0
The Stack: 3 TB of permissively licensed source code—0
Multi-step Problem Solving Through a Verifier: An Empirical Analysis on Model-induced Process Supervision—0
Brevity is the soul of wit: Pruning long files for code generation—0
NExT: Teaching Large Language Models to Reason about Code Execution—0
Thinking Before Running! Efficient Code Generation with Thorough Exploration and Optimal Refinement—0
OpenCodeInstruct: A Large-scale Instruction Tuning Dataset for Code LLMs—0
PythonSaga: Redefining the Benchmark to Evaluate Code Generating LLMs—0
AceCoder: Utilizing Existing Code to Enhance Code Generation—0
Plan for Speed -- Dilated Scheduling for Masked Diffusion Language Models—0
Type-Constrained Code Generation with Language Models—0
PLUM: Improving Code LMs with Execution-Guided On-Policy Preference Learning Driven By Synthetic Test Cases—0
SOEN-101: Code Generation by Emulating Software Process Models Using Large Language Model Agents—0
Uncovering LLM-Generated Code: A Zero-Shot Synthetic Code Detector via Code Rewriting—0
Prompt Baking—0
Q*: Improving Multi-step Reasoning for LLMs with Deliberative Planning—0
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