We present DroidCollection, the most extensive open data suite for training and evaluating machine-generated code detectors, comprising over a million code samples, seven programming languages, outputs from 43 coding models, and three real-world coding domains. Alongside fully AI-generated examples, our collection includes human-AI co-authored code, as well as adversarial examples explicitly crafted to evade detection. Subsequently, we develop DroidDetect, a suite of encoder-only detectors trained using a multi-task objective over DroidCollection. Our experiments show that existing detectors’ performance fails to generalise to diverse coding domains and programming languages outside of their narrow training data. We further demonstrate that while most detectors are easily compromised by humanising the output distributions using superficial prompting and alignment approaches, this problem can be easily amended by training on a small number of adversarial examples. Finally, we demonstrate the effectiveness of metric learning and uncertainty-based resampling as way to enhance detector training on possibly noisy distributions.

Code generation has fast become one of the most popular applications of language models (LMs). Nonetheless, research on multilingual aspects of Code-LMs, such as cross-lingual transfer between different programming languages, language-specific data augmentation, and post-hoc LM adaptation, alongside the exploitation of data sources other than the original textual content, has been much sparser than for their natural language counterparts. In particular, most mainstream Code-LMs have been pre-trained on source code files alone. In this work, we investigate the prospect of leveraging readily available compiler intermediate representations (IR)—shared across programming languages—to improve the multilingual capabilities of Code-LMs and facilitate cross-lingual transfer. To this end, we first compile SLTrans, a parallel dataset consisting of nearly 4M self-contained source code files coupled with their respective intermediate representations. Next, starting from various base Code-LMs (ranging from 1.1B to 7.3B parameters), we carry out continued causal language modelling training on SLTrans, forcing the Code-LMs to (1) learn the IR language and (2) align the IR constructs with respective constructs of various programming languages. Our resulting models, dubbed IRCoder, display sizeable and consistent gains across various code generation tasks and metrics, including prompt robustness, multilingual code completion, code understanding, and instruction following.

We introduce Adapters, an open-source library that unifies parameter-efficient and modular transfer learning in large language models. By integrating 10 diverse adapter methods into a unified interface, Adapters offers ease of use and flexible configuration. Our library allows researchers and practitioners to leverage adapter modularity through composition blocks, enabling the design of complex adapter setups. We demonstrate the library’s efficacy by evaluating its performance against full fine-tuning on various NLP tasks. Adapters provides a powerful tool for addressing the challenges of conventional fine-tuning paradigms and promoting more efficient and modular transfer learning. The library is available via https://adapterhub.ml/adapters.