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Found 8 papers, 7 papers with code
On the Effectiveness of Distillation in Mitigating Backdoors in Pre-trained Encoder
Distillation aims to distill knowledge from a given model (a. k. a the teacher net) and transfer it to another (a. k. a the student net).
Machine Translation Testing via Syntactic Tree Pruning
The results show that STP can accurately find 5, 073 unique erroneous translations in Google Translate and 5, 100 unique erroneous translations in Bing Microsoft Translator (400% more than state-of-the-art techniques), with 64. 5% and 65. 4% precision, respectively.
A Prompt Learning Framework for Source Code Summarization
PromptCS trains a prompt agent that can generate continuous prompts to unleash the potential for LLMs in code summarization.
Abstract Syntax Tree for Programming Language Understanding and Representation: How Far Are We?
To do so, we compare the performance of models trained with code token sequence (Token for short) based code representation and AST-based code representation on three popular types of code-related tasks.
Automatic Code Summarization via ChatGPT: How Far Are We?
To support software developers in understanding and maintaining programs, various automatic code summarization techniques have been proposed to generate a concise natural language comment for a given code snippet.
An Extractive-and-Abstractive Framework for Source Code Summarization
The extractive module in the framework performs a task of extractive code summarization, which takes in the code snippet and predicts important statements containing key factual details.
Code Search based on Context-aware Code Translation
We evaluate the effectiveness of our technique, called TranCS, on the CodeSearchNet corpus with 1, 000 queries.
