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Found 8 papers, 2 papers with code
Accelerating Production LLMs with Combined Token/Embedding Speculators
This technical report describes the design and training of novel speculative decoding draft models, for accelerating the inference speeds of large language models in a production environment.
Automated Code generation for Information Technology Tasks in YAML through Large Language Models
This work focuses on the generation of Ansible-YAML, a widely used markup language for IT Automation.
Study of Distractors in Neural Models of Code
Understanding distractors provide a complementary view of the features' relevance in the predictions of neural models.
VELVET: a noVel Ensemble Learning approach to automatically locate VulnErable sTatements
Automatically locating vulnerable statements in source code is crucial to assure software security and alleviate developers' debugging efforts.
Data-Driven AI Model Signal-Awareness Enhancement and Introspection
AI modeling for source code understanding tasks has been making significant progress, and is being adopted in production development pipelines.
Software Vulnerability Detection via Deep Learning over Disaggregated Code Graph Representation
Identifying vulnerable code is a precautionary measure to counter software security breaches.
Probing Model Signal-Awareness via Prediction-Preserving Input Minimization
We measure the signal awareness of models using a new metric we propose- Signal-aware Recall (SAR).
Learning to map source code to software vulnerability using code-as-a-graph
We explore the applicability of Graph Neural Networks in learning the nuances of source code from a security perspective.
