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Found 6 papers, 4 papers with code
Human-in-the-Loop Synthetic Text Data Inspection with Provenance Tracking
INSPECTOR allows users to group related texts by their transformation provenance, i. e., the transformations applied to the original text, or feature provenance, the linguistic features of the original text.
Privacy-Aware Semantic Cache for Large Language Models
Caching is a natural solution to reduce LLM inference costs on repeated queries which constitute about 31% of the total queries.
ProvFL: Client-Driven Interpretability of Global Model Predictions in Federated Learning
Regardless of the quality of the global model or if it has a fault, understanding the model's origin is equally important for debugging, interpretability, and explainability in federated learning.
FedDefender: Backdoor Attack Defense in Federated Learning
Federated Learning (FL) is a privacy-preserving distributed machine learning technique that enables individual clients (e. g., user participants, edge devices, or organizations) to train a model on their local data in a secure environment and then share the trained model with an aggregator to build a global model collaboratively.
FedDebug: Systematic Debugging for Federated Learning Applications
FedDebug's interactive debugging incurs 1. 2% overhead during training, while it localizes a faulty client in only 2. 1% of a round's training time.
Sibylvariant Transformations for Robust Text Classification
The vast majority of text transformation techniques in NLP are inherently limited in their ability to expand input space coverage due to an implicit constraint to preserve the original class label.
