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Found 11 papers, 5 papers with code
CodeUltraFeedback: An LLM-as-a-Judge Dataset for Aligning Large Language Models to Coding Preferences
We generate responses to the instructions using a pool of 14 diverse LLMs, which we then annotate according to their alignment with five coding preferences using the LLM-as-a-Judge approach with GPT-3. 5, producing both numerical and textual feedback.
CodeLL: A Lifelong Learning Dataset to Support the Co-Evolution of Data and Language Models of Code
Motivated by recent work on lifelong learning applications for language models (LMs) of code, we introduce CodeLL, a lifelong learning dataset focused on code changes.
Exploring Parameter-Efficient Fine-Tuning Techniques for Code Generation with Large Language Models
In this paper, we deliver a comprehensive study of PEFT techniques for LLMs under the automated code generation scenario.
On the Usage of Continual Learning for Out-of-Distribution Generalization in Pre-trained Language Models of Code
We demonstrate that the most commonly used fine-tuning technique from prior work is not robust enough to handle the dynamic nature of APIs, leading to the loss of previously acquired knowledge i. e., catastrophic forgetting.
Towards using Few-Shot Prompt Learning for Automating Model Completion
We propose a simple yet a novel approach to improve completion in domain modeling activities.
Towards Automatically Extracting UML Class Diagrams from Natural Language Specifications
To develop our approach, we create a dataset of UML class diagrams and their English specifications with the help of volunteers.
Ranked #1 on
Model extraction
on UML Classes With Specs
AST-Probe: Recovering abstract syntax trees from hidden representations of pre-trained language models
Using probes, a technique to study the linguistic properties of hidden vector spaces, previous works have shown that these pre-trained language models encode simple linguistic properties in their hidden representations.
Code Sophistication: From Code Recommendation to Logic Recommendation
In this paper, we consider the problem of detecting and recommending such missing behaviors, a task that we call code sophistication.
Better Modeling the Programming World with Code Concept Graphs-augmented Multi-modal Learning
The progress made in code modeling has been tremendous in recent years thanks to the design of natural language processing learning approaches based on state-of-the-art model architectures.
Recommending Metamodel Concepts during Modeling Activities with Pre-Trained Language Models
The design of conceptually sound metamodels that embody proper semantics in relation to the application domain is particularly tedious in Model-Driven Engineering.
Mining Software Components from Object-Oriented APIs
Object-oriented Application Programing Interfaces (APIs) support software reuse by providing pre-implemented functionalities.
