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Showing 176–200 of 264 papers

TitleStatusHype
Does Few-Shot Learning Help LLM Performance in Code Synthesis?—0
Does your data spark joy? Performance gains from domain upsampling at the end of training—0
DOMAINEVAL: An Auto-Constructed Benchmark for Multi-Domain Code Generation—0
Dovetail: A CPU/GPU Heterogeneous Speculative Decoding for LLM inference—0
DSTC: Direct Preference Learning with Only Self-Generated Tests and Code to Improve Code LMs—0
Dynamic Scaling of Unit Tests for Code Reward Modeling—0
Structured Chain-of-Thought Prompting for Code Generation—0
Enhancing LLM-Based Code Generation with Complexity Metrics: A Feedback-Driven Approach—0
Evaluating Large Language Models for Code Review—0
Reasoning Runtime Behavior of a Program with LLM: How Far Are We?—0
Exploring and Evaluating Hallucinations in LLM-Powered Code Generation—0
Falcon: Faster and Parallel Inference of Large Language Models through Enhanced Semi-Autoregressive Drafting and Custom-Designed Decoding Tree—0
From Output to Evaluation: Does Raw Instruction-Tuned Code LLMs Output Suffice for Fill-in-the-Middle Code Generation?—0
Fully Autonomous Programming using Iterative Multi-Agent Debugging with Large Language Models—0
G-Designer: Architecting Multi-agent Communication Topologies via Graph Neural Networks—0
Grammar-Based Code Representation: Is It a Worthy Pursuit for LLMs?—0
GRIN: GRadient-INformed MoE—0
Guaranteed Guess: A Language Modeling Approach for CISC-to-RISC Transpilation with Testing Guarantees—0
Guided Code Generation with LLMs: A Multi-Agent Framework for Complex Code Tasks—0
Guideline Forest: Experience-Induced Multi-Guideline Reasoning with Stepwise Aggregation—0
Importing Phantoms: Measuring LLM Package Hallucination Vulnerabilities—0
Inference-Aware Fine-Tuning for Best-of-N Sampling in Large Language Models—0
InfiFusion: A Unified Framework for Enhanced Cross-Model Reasoning via LLM Fusion—0
Interactive Code Generation via Test-Driven User-Intent Formalization—0
Isolating Language-Coding from Problem-Solving: Benchmarking LLMs with PseudoEval—0
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