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Large Language Model-Aware In-Context Learning for Code Generation
Large language models (LLMs) have shown impressive in-context learning (ICL) ability in code generation.
Poison Attack and Defense on Deep Source Code Processing Models
The attackers aim to inject insidious backdoors into models by poisoning the training data with poison samples.
Learning to Represent Programs with Heterogeneous Graphs
To address the information of node and edge types, we bring the idea of heterogeneous graphs to learning on source code and present a new formula of building heterogeneous program graphs from ASTs with additional type information for nodes and edges.
Generating Fluent Adversarial Examples for Natural Languages
Efficiently building an adversarial attacker for natural language processing (NLP) tasks is a real challenge.
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