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Found 7 papers, 1 papers with code
Filter bubbles and affective polarization in user-personalized large language model outputs
In this work, we explore how prompting a leading large language model, ChatGPT-3. 5, with a user's political affiliation prior to asking factual questions about public figures and organizations leads to differing results.
TwERC: High Performance Ensembled Candidate Generation for Ads Recommendation at Twitter
We focus on the candidate generation phase of a large-scale ads recommendation problem in this paper, and present a machine learning first heterogeneous re-architecture of this stage which we term TwERC.
Random Isn't Always Fair: Candidate Set Imbalance and Exposure Inequality in Recommender Systems
Traditionally, recommender systems operate by returning a user a set of items, ranked in order of estimated relevance to that user.
Measuring Disparate Outcomes of Content Recommendation Algorithms with Distributional Inequality Metrics
We show that we can use these metrics to identify content suggestion algorithms that contribute more strongly to skewed outcomes between users.
Automated Vulnerability Detection in Source Code Using Deep Representation Learning
The labeled dataset is available at: https://osf. io/d45bw/.
Learning to Repair Software Vulnerabilities with Generative Adversarial Networks
Motivated by the problem of automated repair of software vulnerabilities, we propose an adversarial learning approach that maps from one discrete source domain to another target domain without requiring paired labeled examples or source and target domains to be bijections.
Automated software vulnerability detection with machine learning
We then compare methods applied directly to source code with methods applied to artifacts extracted from the build process, finding that source-based models perform better.
