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Found 9 papers, 1 papers with code
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.
BAM: Box Abstraction Monitors for Real-time OoD Detection in Object Detection
Nevertheless, integrating OoD detection into state-of-the-art (SOTA) object detection DNNs poses significant challenges, partly due to the complexity introduced by the SOTA OoD construction methods, which require the modification of DNN architecture and the introduction of complex loss functions.
A Digital Twin prototype for traffic sign recognition of a learning-enabled autonomous vehicle
In this paper, we present a novel digital twin prototype for a learning-enabled self-driving vehicle.
What, Indeed, is an Achievable Provable Guarantee for Learning-Enabled Safety Critical Systems
Machine learning has made remarkable advancements, but confidently utilising learning-enabled components in safety-critical domains still poses challenges.
Towards Rigorous Design of OoD Detectors
Out-of-distribution (OoD) detection techniques are instrumental for safety-related neural networks.
A Survey of Safety and Trustworthiness of Large Language Models through the Lens of Verification and Validation
Large Language Models (LLMs) have exploded a new heatwave of AI for their ability to engage end-users in human-level conversations with detailed and articulate answers across many knowledge domains.
Prioritizing Corners in OoD Detectors via Symbolic String Manipulation
We consider the definition of "in-distribution" characterized in the feature space by a union of hyperrectangles learned from the training dataset.
Customizable Reference Runtime Monitoring of Neural Networks using Resolution Boxes
Classification neural networks fail to detect inputs that do not fall inside the classes they have been trained for.
Polynomial invariants by linear algebra
We present in this paper a new technique for generating polynomial invariants, divided in two independent parts : a procedure that reduces polynomial assignments composed loops analysis to linear loops under certain hypotheses and a procedure for generating inductive invariants for linear loops.
Logic in Computer Science
