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Showing 176–200 of 264 papers

TitleStatusHype
LLaDA 1.5: Variance-Reduced Preference Optimization for Large Language Diffusion Models—0
LoRA-Mixer: Coordinate Modular LoRA Experts Through Serial Attention Routing—0
LORD: Low Rank Decomposition Of Monolingual Code LLMs For One-Shot Compression—0
Low-Cost Language Models: Survey and Performance Evaluation on Python Code Generation—0
MaPPing Your Model: Assessing the Impact of Adversarial Attacks on LLM-based Programming Assistants—0
USCD: Improving Code Generation of LLMs by Uncertainty-Aware Selective Contrastive Decoding—0
Memorization or Interpolation ? Detecting LLM Memorization through Input Perturbation Analysis—0
MojoBench: Language Modeling and Benchmarks for Mojo—0
Mutation-based Consistency Testing for Evaluating the Code Understanding Capability of LLMs—0
NExT: Teaching Large Language Models to Reason about Code Execution—0
NoFunEval: Funny How Code LMs Falter on Requirements Beyond Functional Correctness—0
On the Limitations of Embedding Based Methods for Measuring Functional Correctness for Code Generation—0
OpenCodeInstruct: A Large-scale Instruction Tuning Dataset for Code LLMs—0
PanGu-Coder2: Boosting Large Language Models for Code with Ranking Feedback—0
Past as a Guide: Leveraging Retrospective Learning for Python Code Completion—0
PERC: Plan-As-Query Example Retrieval for Underrepresented Code Generation—0
Piloting Copilot, Codex, and StarCoder2: Hot Temperature, Cold Prompts, or Black Magic?—0
Plan for Speed -- Dilated Scheduling for Masked Diffusion Language Models—0
PLUM: Improving Code LMs with Execution-Guided On-Policy Preference Learning Driven By Synthetic Test Cases—0
Prior Prompt Engineering for Reinforcement Fine-Tuning—0
Qiskit Code Assistant: Training LLMs for generating Quantum Computing Code—0
Qiskit HumanEval: An Evaluation Benchmark For Quantum Code Generative Models—0
QualityFlow: An Agentic Workflow for Program Synthesis Controlled by LLM Quality Checks—0
Reactor Mk.1 performances: MMLU, HumanEval and BBH test results—0
Reasoning-as-Logic-Units: Scaling Test-Time Reasoning in Large Language Models Through Logic Unit Alignment—0
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