xCodeEval

The ability to solve problems is a hallmark of intelligence and has been an enduring goal in AI. AI systems that can create programs as solutions to problems or assist developers in writing programs can increase productivity and make programming more accessible. Recently, pre-trained large language models have shown im

Papers using xCodeEval (11)

An Iterative Test-and-Repair Framework for Competitive Code Generation2026CollabCoder: Plan-Code Co-Evolution via Collaborative Decision-Making for Efficient Code Generation2026HELO-APR: Enhancing Low-Resource Program Repair through Cross-Lingual Knowledge Transfer2026CodeEval: A pedagogical approach for targeted evaluation of code-trained Large Language Models2026Collaborative Agents for Automated Program Repair in Ruby2025MapCoder-Lite: Distilling Multi-Agent Coding into a Single Small LLM2025Integrating Rules and Semantics for LLM-Based C-to-Rust Translation2025Unlocking LLM Repair Capabilities Through Cross-Language Translation and Multi-Agent Refinement2025MapCoder: Multi-Agent Code Generation for Competitive Problem Solving2024 · 23 citesxCodeEval: A Large Scale Multilingual Multitask Benchmark for Code Understanding, Generation, Translation and Retrieval2023 · 10 citesDivide-and-Conquer Meets Consensus: Unleashing the Power of Functions in Code Generation2024 · 1 cites