paper-with-me

홈 › Papers

CoDesc: A Large Code–Description Parallel Dataset

2021-08-01 · Findings (ACL) 2021 8 · Masum Hasan, Tanveer Muttaqueen, Abdullah Al Ishtiaq, Kazi Sajeed Mehrab, Md. Mahim Anjum Haque, Tahmid Hasan, Wasi Ahmad, Anindya Iqbal, Rifat Shahriyar
📄 PDF Abstract BibTeX

Code (1)

csebuetnlp/CoDesc 공식 구현 pytorch

Similar Papers 제목 키워드 기반

CoDesc: A Large Code-Description Parallel Dataset

2021-05-29 · Masum Hasan, Tanveer Muttaqueen, Abdullah Al Ishtiaq, Kazi Sajeed Mehrab 외

Translation between natural language and source code can help software development by enabling developers to comprehend, ideate, search, and write computer programs in natural language. Despite growing interest from the …

Code SearchCode SummarizationSource Code SummarizationTranslation

CodeScaler: Scaling Code LLM Training and Test-Time Inference via Reward Models

2026-02-04 · Xiao Zhu, Xinyu Zhou, Boyu Zhu, Hanxu Hu 외 arxiv

Reinforcement Learning from Verifiable Rewards (RLVR) has driven recent progress in code large language models by leveraging execution-based feedback from unit tests, but its scalability is fundamentally constrained by t…

Reinforcement LearningCode Generation

CodeSCM: Causal Analysis for Multi-Modal Code Generation

2025-02-07 · Mukur Gupta, Noopur Bhatt, Suman Jana

In this paper, we propose CodeSCM, a Structural Causal Model (SCM) for analyzing multi-modal code generation using large language models (LLMs). By applying interventions to CodeSCM, we measure the causal effects of diff…

Code Generation

CodeScholar: Growing Idiomatic Code Examples

2023-12-23 · Manish Shetty, Koushik Sen, Ion Stoica

Programmers often search for usage examples for API methods. A tool that could generate realistic, idiomatic, and contextual usage examples for one or more APIs would be immensely beneficial to developers. Such a tool wo…

Program Synthesis

CodeScope: An Execution-based Multilingual Multitask Multidimensional Benchmark for Evaluating LLMs on Code Understanding and Generation

2023-11-14 · Weixiang Yan, Haitian Liu, Yunkun Wang, Yunzhe Li 외

Large Language Models (LLMs) have demonstrated remarkable performance on assisting humans in programming and facilitating programming automation. However, existing benchmarks for evaluating the code understanding and gen…

Code Generation