ObscuraCoder: Powering Efficient Code LM Pre-Training Via Obfuscation Grounding

1 code implementation • 27 Mar 2025 • Indraneil Paul, Haoyi Yang, Goran Glavaš, Kristian Kersting, Iryna Gurevych

Language models (LMs) have become a staple of the code-writing toolbox.

Code Completion Library-Oriented Code Generation

EMMA-500: Enhancing Massively Multilingual Adaptation of Large Language Models

2 code implementations • 26 Sep 2024 • Shaoxiong Ji, Zihao Li, Indraneil Paul, Jaakko Paavola, Peiqin Lin, Pinzhen Chen, Dayyán O'Brien, Hengyu Luo, Hinrich Schütze, Jörg Tiedemann, Barry Haddow

In this work, we introduce EMMA-500, a large-scale multilingual language model continue-trained on texts across 546 languages designed for enhanced multilingual performance, focusing on improving language coverage for low-resource languages.

Cross-Lingual Transfer Language Modeling +1

BigCodeBench: Benchmarking Code Generation with Diverse Function Calls and Complex Instructions

3 code implementations • 22 Jun 2024 • Terry Yue Zhuo, Minh Chien Vu, Jenny Chim, Han Hu, Wenhao Yu, Ratnadira Widyasari, Imam Nur Bani Yusuf, Haolan Zhan, Junda He, Indraneil Paul, Simon Brunner, Chen Gong, Thong Hoang, Armel Randy Zebaze, Xiaoheng Hong, Wen-Ding Li, Jean Kaddour, Ming Xu, Zhihan Zhang, Prateek Yadav, Naman jain, Alex Gu, Zhoujun Cheng, Jiawei Liu, Qian Liu, Zijian Wang, Binyuan Hui, Niklas Muennighoff, David Lo, Daniel Fried, Xiaoning Du, Harm de Vries, Leandro von Werra

In addition, using multiple tools to solve a task needs compositional reasoning by accurately understanding complex instructions.

Benchmarking Code Generation

IRCoder: Intermediate Representations Make Language Models Robust Multilingual Code Generators

1 code implementation • 6 Mar 2024 • Indraneil Paul, Goran Glavaš, Iryna Gurevych

In particular, most mainstream Code-LMs have been pre-trained on source code files alone.

Code Completion Code Generation +4

StarCoder 2 and The Stack v2: The Next Generation

4 code implementations • 29 Feb 2024 • Anton Lozhkov, Raymond Li, Loubna Ben allal, Federico Cassano, Joel Lamy-Poirier, Nouamane Tazi, Ao Tang, Dmytro Pykhtar, Jiawei Liu, Yuxiang Wei, Tianyang Liu, Max Tian, Denis Kocetkov, Arthur Zucker, Younes Belkada, Zijian Wang, Qian Liu, Dmitry Abulkhanov, Indraneil Paul, Zhuang Li, Wen-Ding Li, Megan Risdal, Jia Li, Jian Zhu, Terry Yue Zhuo, Evgenii Zheltonozhskii, Nii Osae Osae Dade, Wenhao Yu, Lucas Krauß, Naman jain, Yixuan Su, Xuanli He, Manan Dey, Edoardo Abati, Yekun Chai, Niklas Muennighoff, Xiangru Tang, Muhtasham Oblokulov, Christopher Akiki, Marc Marone, Chenghao Mou, Mayank Mishra, Alex Gu, Binyuan Hui, Tri Dao, Armel Zebaze, Olivier Dehaene, Nicolas Patry, Canwen Xu, Julian McAuley, Han Hu, Torsten Scholak, Sebastien Paquet, Jennifer Robinson, Carolyn Jane Anderson, Nicolas Chapados, Mostofa Patwary, Nima Tajbakhsh, Yacine Jernite, Carlos Muñoz Ferrandis, Lingming Zhang, Sean Hughes, Thomas Wolf, Arjun Guha, Leandro von Werra, Harm de Vries

Our large model, StarCoder2- 15B, significantly outperforms other models of comparable size.

Code Completion Code Generation +1

Adapters: A Unified Library for Parameter-Efficient and Modular Transfer Learning

1 code implementation • 18 Nov 2023 • Clifton Poth, Hannah Sterz, Indraneil Paul, Sukannya Purkayastha, Leon Engländer, Timo Imhof, Ivan Vulić, Sebastian Ruder, Iryna Gurevych, Jonas Pfeiffer

We introduce Adapters, an open-source library that unifies parameter-efficient and modular transfer learning in large language models.

Transfer Learning

What sets Verified Users apart? Insights, Analysis and Prediction of Verified Users on Twitter

no code implementations • 12 Mar 2019 • Indraneil Paul, Abhinav Khattar, Shaan Chopra, Ponnurangam Kumaraguru, Manish Gupta

The aim of the paper is two-fold: First, we test if discerning the verification status of a handle from profile metadata and content features is feasible.