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Found 9 papers, 4 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.
Direct Training Needs Regularisation: Anytime Optimal Inference Spiking Neural Network
Notably, with STR and cutoff, SNN achieves 2. 14 to 2. 89 faster in inference compared to the pre-configured timestep with near-zero accuracy drop of 0. 50% to 0. 64% over the event-based datasets.
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.
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.
Safety Analysis in the Era of Large Language Models: A Case Study of STPA using ChatGPT
Can safety analysis make use of Large Language Models (LLMs)?
Continual Learning for CTR Prediction: A Hybrid Approach
To address this problem, we formulate CTR prediction as a continual learning task and propose COLF, a hybrid COntinual Learning Framework for CTR prediction, which has a memory-based modular architecture that is designed to adapt, learn and give predictions continuously when faced with non-stationary drifting click data streams.
Deep Position-wise Interaction Network for CTR Prediction
Click-through rate (CTR) prediction plays an important role in online advertising and recommender systems.
A Deep Spatio-Temporal Fuzzy Neural Network for Passenger Demand Prediction
In spite of its importance, passenger demand prediction is a highly challenging problem, because the demand is simultaneously influenced by the complex interactions among many spatial and temporal factors and other external factors such as weather.
Bandit Learning with Implicit Feedback
Implicit feedback, such as user clicks, although abundant in online information service systems, does not provide substantial evidence on users' evaluation of system's output.
