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Found 5 papers, 1 papers with code
Adaptive Guardrails For Large Language Models via Trust Modeling and In-Context Learning
Guardrails have become an integral part of Large language models (LLMs), by moderating harmful or toxic response in order to maintain LLMs' alignment to human expectations.
A Combination Model for Time Series Prediction using LSTM via Extracting Dynamic Features Based on Spatial Smoothing and Sequential General Variational Mode Decomposition
In order to solve the problems such as difficult to extract effective features and low accuracy of sales volume prediction caused by complex relationships such as market sales volume in time series prediction, we proposed a time series prediction method of market sales volume based on Sequential General VMD and spatial smoothing Long short-term memory neural network (SS-LSTM) combination model.
Safeguarding Large Language Models: A Survey
In the burgeoning field of Large Language Models (LLMs), developing a robust safety mechanism, colloquially known as "safeguards" or "guardrails", has become imperative to ensure the ethical use of LLMs within prescribed boundaries.
Explainable Global Wildfire Prediction Models using Graph Neural Networks
Wildfire prediction has become increasingly crucial due to the escalating impacts of climate change.
Building Guardrails for Large Language Models
As Large Language Models (LLMs) become more integrated into our daily lives, it is crucial to identify and mitigate their risks, especially when the risks can have profound impacts on human users and societies.
