Search Results for author:
Found 11 papers, 4 papers with code
Online Safety Analysis for LLMs: a Benchmark, an Assessment, and a Path Forward
To bridge this gap, we conduct in this work a comprehensive evaluation of the effectiveness of existing online safety analysis methods on LLMs.
PromptCharm: Text-to-Image Generation through Multi-modal Prompting and Refinement
However, prompting remains challenging for novice users due to the complexity of the stable diffusion model and the non-trivial efforts required for iteratively editing and refining the text prompts.
LUNA: A Model-Based Universal Analysis Framework for Large Language Models
the trustworthiness perspective, is bound to and enriches the abstract model with semantics, which enables more detailed analysis applications for diverse purposes.
MSAC: Multiple Speech Attribute Control Method for Reliable Speech Emotion Recognition
Despite notable progress, speech emotion recognition (SER) remains challenging due to the intricate and ambiguous nature of speech emotion, particularly in wild world.
Look Before You Leap: An Exploratory Study of Uncertainty Measurement for Large Language Models
In particular, we experiment with twelve uncertainty estimation methods and four LLMs on four prominent natural language processing (NLP) tasks to investigate to what extent uncertainty estimation techniques could help characterize the prediction risks of LLMs.
Rethinking the Role of Pre-ranking in Large-scale E-Commerce Searching System
As such, the metric of a pre-ranking model follows the ranking model using Area Under ROC (AUC) for offline evaluation.
DeepLens: Interactive Out-of-distribution Data Detection in NLP Models
In this work, we propose DeepLens, an interactive system that helps users detect and explore OOD issues in massive text corpora.
DeepSeer: Interactive RNN Explanation and Debugging via State Abstraction
The core of DeepSeer is a state abstraction method that bundles semantically similar hidden states in an RNN model and abstracts the model as a finite state machine.
An Exploratory Study of AI System Risk Assessment from the Lens of Data Distribution and Uncertainty
More importantly, it also lacks systematic investigation on how to perform the risk assessment for AI systems from unit level to system level.
PatchCensor: Patch Robustness Certification for Transformers via Exhaustive Testing
Vision Transformer (ViT) is known to be highly nonlinear like other classical neural networks and could be easily fooled by both natural and adversarial patch perturbations.
Neuron-level Structured Pruning using Polarization Regularizer
Experimentally, we show that structured pruning using polarization regularizer achieves much better results than using L1 regularizer.
