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Found 11 papers, 10 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.
TransformCode: A Contrastive Learning Framework for Code Embedding via Subtree Transformation
Our framework has several advantages over existing methods: (1) It is flexible and adaptable, because it can easily be extended to other downstream tasks that require code representation (such as code-clone detection and classification); (2) it is efficient and scalable, because it does not require a large model or a large amount of training data, and it can support any programming language; (3) it is not limited to unsupervised learning, but can also be applied to some supervised learning tasks by incorporating task-specific labels or objectives; and (4) it can also adjust the number of encoder parameters based on computing resources.
Self-Refined Large Language Model as Automated Reward Function Designer for Deep Reinforcement Learning in Robotics
Then, the performance of the reward function is assessed, and the results are presented back to the LLM for guiding its self-refinement process.
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.
Certifying Robustness of Convolutional Neural Networks with Tight Linear Approximation
The robustness of neural network classifiers is becoming important in the safety-critical domain and can be quantified by robustness verification.
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.
