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cs.LG2025
Rational Tuning of LLM Cascades via Probabilistic Modeling
Michael J. Zellinger, Matt Thomson
Understanding the reliability of large language models (LLMs) has recently garnered significant attention. Given LLMs' propensity to hallucinate, as well as their high sensitivity…
cs.LG2025
Natural Language-Based Synthetic Data Generation for Cluster Analysis
Michael J. Zellinger, Peter Bühlmann
Cluster analysis relies on effective benchmarks for evaluating and comparing different algorithms. Simulation studies on synthetic data are popular because important features of th…
cs.LG2024
Efficiently Deploying LLMs with Controlled Risk
Michael J. Zellinger, Matt Thomson
Deploying large language models in production requires simultaneous attention to efficiency and risk control. Prior work has shown the possibility to cut costs while maintaining si…