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Found 6 papers, 1 papers with code
An Empirical Study on the Fairness of Pre-trained Word Embeddings
Pre-trained word embedding models are easily distributed and applied, as they alleviate users from the effort to train models themselves.
A Novel Approach for Automatic Program Repair using Round-Trip Translation with Large Language Models
We investigate whether this correction capability of Large Language Models (LLMs) extends to Automatic Program Repair (APR).
An Exploratory Literature Study on Sharing and Energy Use of Language Models for Source Code
Large language models trained on source code can support a variety of software development tasks, such as code recommendation and program repair.
The EarlyBIRD Catches the Bug: On Exploiting Early Layers of Encoder Models for More Efficient Code Classification
These findings show that early layers can be used to obtain better results using the same resources, as well as to reduce resource usage during fine-tuning and inference.
Enhanced Fairness Testing via Generating Effective Initial Individual Discriminatory Instances
In this paper, we propose an approach for the selection of the initial seeds to generate IDIs for fairness testing.
Bias Mitigation for Machine Learning Classifiers: A Comprehensive Survey
How many datasets are used for evaluating bias mitigation methods?
