Zobrazeno 1 - 10
of 19
pro vyhledávání: '"Cassano, Federico"'
Autor:
Wang, Evan, Cassano, Federico, Wu, Catherine, Bai, Yunfeng, Song, Will, Nath, Vaskar, Han, Ziwen, Hendryx, Sean, Yue, Summer, Zhang, Hugh
While scaling training compute has led to remarkable improvements in large language models (LLMs), scaling inference compute has not yet yielded analogous gains. We hypothesize that a core missing component is a lack of diverse LLM outputs, leading t
Externí odkaz:
http://arxiv.org/abs/2409.03733
Autor:
Loughridge, Chloe, Sun, Qinyi, Ahrenbach, Seth, Cassano, Federico, Sun, Chuyue, Sheng, Ying, Mudide, Anish, Misu, Md Rakib Hossain, Amin, Nada, Tegmark, Max
We introduce DafnyBench, the largest benchmark of its kind for training and evaluating machine learning systems for formal software verification. We test the ability of LLMs such as GPT-4 and Claude 3 to auto-generate enough hints for the Dafny forma
Externí odkaz:
http://arxiv.org/abs/2406.08467
Autor:
Lozhkov, Anton, Li, Raymond, Allal, Loubna Ben, Cassano, Federico, Lamy-Poirier, Joel, Tazi, Nouamane, Tang, Ao, Pykhtar, Dmytro, Liu, Jiawei, Wei, Yuxiang, Liu, Tianyang, Tian, Max, Kocetkov, Denis, Zucker, Arthur, Belkada, Younes, Wang, Zijian, Liu, Qian, Abulkhanov, Dmitry, Paul, Indraneil, Li, Zhuang, Li, Wen-Ding, Risdal, Megan, Li, Jia, Zhu, Jian, Zhuo, Terry Yue, Zheltonozhskii, Evgenii, Dade, Nii Osae Osae, Yu, Wenhao, Krauß, Lucas, Jain, Naman, Su, Yixuan, He, Xuanli, Dey, Manan, Abati, Edoardo, Chai, Yekun, Muennighoff, Niklas, Tang, Xiangru, Oblokulov, Muhtasham, Akiki, Christopher, Marone, Marc, Mou, Chenghao, Mishra, Mayank, Gu, Alex, Hui, Binyuan, Dao, Tri, Zebaze, Armel, Dehaene, Olivier, Patry, Nicolas, Xu, Canwen, McAuley, Julian, Hu, Han, Scholak, Torsten, Paquet, Sebastien, Robinson, Jennifer, Anderson, Carolyn Jane, Chapados, Nicolas, Patwary, Mostofa, Tajbakhsh, Nima, Jernite, Yacine, Ferrandis, Carlos Muñoz, Zhang, Lingming, Hughes, Sean, Wolf, Thomas, Guha, Arjun, von Werra, Leandro, de Vries, Harm
The BigCode project, an open-scientific collaboration focused on the responsible development of Large Language Models for Code (Code LLMs), introduces StarCoder2. In partnership with Software Heritage (SWH), we build The Stack v2 on top of the digita
Externí odkaz:
http://arxiv.org/abs/2402.19173
Autor:
Brandfonbrener, David, Henniger, Simon, Raja, Sibi, Prasad, Tarun, Loughridge, Chloe, Cassano, Federico, Hu, Sabrina Ruixin, Yang, Jianang, Byrd, William E., Zinkov, Robert, Amin, Nada
Large Language Models (LLMs) can generate useful code, but often the code they generate cannot be trusted to be sound. In this paper, we present VerMCTS, an approach to begin to resolve this issue by generating verified programs in Dafny and Coq. Ver
Externí odkaz:
http://arxiv.org/abs/2402.08147
Autor:
Cassano, Federico, Li, Luisa, Sethi, Akul, Shinn, Noah, Brennan-Jones, Abby, Ginesin, Jacob, Berman, Edward, Chakhnashvili, George, Lozhkov, Anton, Anderson, Carolyn Jane, Guha, Arjun
A significant amount of research is focused on developing and evaluating large language models for a variety of code synthesis tasks. These include synthesizing code from natural language, synthesizing tests from code, and synthesizing explanations o
Externí odkaz:
http://arxiv.org/abs/2312.12450
Software developers typically rely upon a large network of dependencies to build their applications. For instance, the NPM package repository contains over 3 million packages and serves tens of billions of downloads weekly. Understanding the structur
Externí odkaz:
http://arxiv.org/abs/2308.12545
Autor:
Cassano, Federico, Gouwar, John, Lucchetti, Francesca, Schlesinger, Claire, Freeman, Anders, Anderson, Carolyn Jane, Feldman, Molly Q, Greenberg, Michael, Jangda, Abhinav, Guha, Arjun
Over the past few years, Large Language Models of Code (Code LLMs) have started to have a significant impact on programming practice. Code LLMs are also emerging as building blocks for research in programming languages and software engineering. Howev
Externí odkaz:
http://arxiv.org/abs/2308.09895
TypeScript and Python are two programming languages that support optional type annotations, which are useful but tedious to introduce and maintain. This has motivated automated type prediction: given an untyped program, produce a well-typed output pr
Externí odkaz:
http://arxiv.org/abs/2305.17145
Memory safety is a cornerstone of secure and robust software systems, as it prevents a wide range of vulnerabilities and exploitation techniques. Among these, we focus on Return-Oriented Programming (ROP). ROP works as such: the attacker takes contro
Externí odkaz:
http://arxiv.org/abs/2305.06092
The NPM package repository contains over two million packages and serves tens of billions of downloads per-week. Nearly every single JavaScript application uses the NPM package manager to install packages from the NPM repository. NPM relies on a "sem
Externí odkaz:
http://arxiv.org/abs/2304.00394