LLM surrogate beliefs under sparse observations depend on prompts and query protocols, with structural prompts as priors, pointwise vs joint querying producing different beliefs, and sequential evidence causing non-monotonic updates that affect acquisition and regret.
On calibration of modern neural networks
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A physics-informed graph variational autoencoder jointly predicts modal frequencies, damping, and shapes from PSD data of trusses with uncertainty quantification and orthogonality constraints.
Negative-capable ridge regression uses controlled negative regularization as anti-shrinkage to increase effective complexity along weak eigendirections and mitigate underfitting in small-data regression.
A methodology decomposes total uncertainty in regional risk assessment into contributions from probabilistic exposure characterization and other sources using analytical and simulation approaches.
Survey organizes LLM trustworthiness into seven categories and 29 sub-categories, measures eight sub-categories on popular models, and finds that more aligned models generally score higher but with varying effectiveness.
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Elicitation Matters: How Prompts and Query Protocols Shape LLM Surrogates under Sparse Observations
LLM surrogate beliefs under sparse observations depend on prompts and query protocols, with structural prompts as priors, pointwise vs joint querying producing different beliefs, and sequential evidence causing non-monotonic updates that affect acquisition and regret.
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A Physics-Aware Variational Graph Autoencoder for Joint Modal Identification with Uncertainty Quantification
A physics-informed graph variational autoencoder jointly predicts modal frequencies, damping, and shapes from PSD data of trusses with uncertainty quantification and orthogonality constraints.
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A Ridge Too Far: Correcting Over-Shrinkage via Negative Regularization
Negative-capable ridge regression uses controlled negative regularization as anti-shrinkage to increase effective complexity along weak eigendirections and mitigate underfitting in small-data regression.
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Quantifying Exposure Information Uncertainty in Regional Risk Assessment
A methodology decomposes total uncertainty in regional risk assessment into contributions from probabilistic exposure characterization and other sources using analytical and simulation approaches.
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Trustworthy LLMs: a Survey and Guideline for Evaluating Large Language Models' Alignment
Survey organizes LLM trustworthiness into seven categories and 29 sub-categories, measures eight sub-categories on popular models, and finds that more aligned models generally score higher but with varying effectiveness.