ICL in LLMs shows a sharp ceiling on categorical distributions for high-cardinality tabular data, failing to reproduce rare classes despite examples, while numerical fidelity improves.
arXiv preprint arXiv:2312.12112 , year=
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LLM-generated synthetic datasets steered uniformly across a 2D performance space defined by two landmark algorithms improve meta-learner performance on algorithm selection for regression tasks.
A structured literature survey categorizing generative AI (autoencoders, GANs, diffusion models, LLMs) and federated learning uses in IDS, covering tasks like synthetic data generation and anomaly detection plus open challenges.
citing papers explorer
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Categorical Prior Lock-in: Why In-Context Learning Fails for Structured Data
ICL in LLMs shows a sharp ceiling on categorical distributions for high-cardinality tabular data, failing to reproduce rare classes despite examples, while numerical fidelity improves.
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LLM-Driven Performance-Space Augmentation for Meta-Learning-Based Algorithm Selection
LLM-generated synthetic datasets steered uniformly across a 2D performance space defined by two landmark algorithms improve meta-learner performance on algorithm selection for regression tasks.
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Generative AI and Federated Learning for Intrusion Detection Systems: A Survey
A structured literature survey categorizing generative AI (autoencoders, GANs, diffusion models, LLMs) and federated learning uses in IDS, covering tasks like synthetic data generation and anomaly detection plus open challenges.