This paper introduces a systems-level conceptual framing and a three-level taxonomy (intra-model, system-level, socio-technical) for uncertainty propagation in compound LLM applications, along with engineering insights and open challenges.
Rational tuning of LLM cascades via probabilistic modeling
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An edge-cloud-expert LLM cascade for telecom knowledge systems minimizes processing cost subject to misalignment-risk bounds via multiple hypothesis testing on knowledge and confidence scores.
AutoRelAnnotator routes queries through fine-tuned classifier cascades with isotonic calibration to deliver high-accuracy relevance labels at roughly half the compute cost while adding a small accuracy gain.
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Uncertainty Propagation in LLM-Based Systems
This paper introduces a systems-level conceptual framing and a three-level taxonomy (intra-model, system-level, socio-technical) for uncertainty propagation in compound LLM applications, along with engineering insights and open challenges.
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Reliable LLM-Based Edge-Cloud-Expert Cascades for Telecom Knowledge Systems
An edge-cloud-expert LLM cascade for telecom knowledge systems minimizes processing cost subject to misalignment-risk bounds via multiple hypothesis testing on knowledge and confidence scores.
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AutoRelAnnotator: Calibrated Model Cascades for Cost-Efficient Relevance Evaluation in Sponsored Search
AutoRelAnnotator routes queries through fine-tuned classifier cascades with isotonic calibration to deliver high-accuracy relevance labels at roughly half the compute cost while adding a small accuracy gain.