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of 306
pro vyhledávání: '"TIAN Yuchen"'
Recent advancements in large language models (LLMs) have showcased impressive code generation capabilities, primarily evaluated through language-to-code benchmarks. However, these benchmarks may not fully capture a model's code understanding abilitie
Externí odkaz:
http://arxiv.org/abs/2408.10718
Autor:
Tian, Yuchen, Yan, Weixiang, Yang, Qian, Zhao, Xuandong, Chen, Qian, Wang, Wen, Luo, Ziyang, Ma, Lei, Song, Dawn
Large Language Models (LLMs) have made significant progress in code generation, offering developers groundbreaking automated programming support. However, LLMs often generate code that is syntactically correct and even semantically plausible, but may
Externí odkaz:
http://arxiv.org/abs/2405.00253
Autor:
Tian, Yuchen, Moreno, Ari R. Ortiz, Chipaux, Mayeul, Wu, Kaiqi, Martinez, Felipe P. Perona, Shirzad, Hoda, Hamoh, Thamir, Mzyk, Aldona, van Rijn, Patrick, Schirhagl, Romana
Diamond is increasingly popular because of its unique material properties. Diamond defects called nitrogen vacancy (NV) centers allow measurements with unprecedented sensitivity. However, to achieve ideal sensing performance NV centers need to be wit
Externí odkaz:
http://arxiv.org/abs/2404.11961
Programming often involves converting detailed and complex specifications into code, a process during which developers typically utilize visual aids to more effectively convey concepts. While recent developments in Large Multimodal Models have demons
Externí odkaz:
http://arxiv.org/abs/2404.09486
Autor:
Athiwaratkun, Ben, Wang, Shiqi, Shang, Mingyue, Tian, Yuchen, Wang, Zijian, Gonugondla, Sujan Kumar, Gouda, Sanjay Krishna, Kwiatowski, Rob, Nallapati, Ramesh, Xiang, Bing
Generative models, widely utilized in various applications, can often struggle with prompts corresponding to partial tokens. This struggle stems from tokenization, where partial tokens fall out of distribution during inference, leading to incorrect o
Externí odkaz:
http://arxiv.org/abs/2403.08688
Recent code translation techniques exploit neural machine translation models to translate source code from one programming language to another to satisfy production compatibility or to improve efficiency of codebase maintenance. Most existing code tr
Externí odkaz:
http://arxiv.org/abs/2310.04951
Autor:
Ding, Hantian, Kumar, Varun, Tian, Yuchen, Wang, Zijian, Kwiatkowski, Rob, Li, Xiaopeng, Ramanathan, Murali Krishna, Ray, Baishakhi, Bhatia, Parminder, Sengupta, Sudipta, Roth, Dan, Xiang, Bing
Large language models trained on code have shown great potential to increase productivity of software developers. Several execution-based benchmarks have been proposed to evaluate functional correctness of model-generated code on simple programming p
Externí odkaz:
http://arxiv.org/abs/2306.03203
Autor:
Wei, Xiaokai, Gonugondla, Sujan, Ahmad, Wasi, Wang, Shiqi, Ray, Baishakhi, Qian, Haifeng, Li, Xiaopeng, Kumar, Varun, Wang, Zijian, Tian, Yuchen, Sun, Qing, Athiwaratkun, Ben, Shang, Mingyue, Ramanathan, Murali Krishna, Bhatia, Parminder, Xiang, Bing
ML-powered code generation aims to assist developers to write code in a more productive manner, by intelligently generating code blocks based on natural language prompts. Recently, large pretrained deep learning models have substantially pushed the b
Externí odkaz:
http://arxiv.org/abs/2303.05378
Autor:
Athiwaratkun, Ben, Gouda, Sanjay Krishna, Wang, Zijian, Li, Xiaopeng, Tian, Yuchen, Tan, Ming, Ahmad, Wasi Uddin, Wang, Shiqi, Sun, Qing, Shang, Mingyue, Gonugondla, Sujan Kumar, Ding, Hantian, Kumar, Varun, Fulton, Nathan, Farahani, Arash, Jain, Siddhartha, Giaquinto, Robert, Qian, Haifeng, Ramanathan, Murali Krishna, Nallapati, Ramesh, Ray, Baishakhi, Bhatia, Parminder, Sengupta, Sudipta, Roth, Dan, Xiang, Bing
We present new benchmarks on evaluation code generation models: MBXP and Multilingual HumanEval, and MathQA-X. These datasets cover over 10 programming languages and are generated using a scalable conversion framework that transpiles prompts and test
Externí odkaz:
http://arxiv.org/abs/2210.14868
Publikováno v:
In Journal of Colloid And Interface Science 15 December 2024 676:283-297
