Search Results for author:
Found 10 papers, 6 papers with code
Introducing v0.5 of the AI Safety Benchmark from MLCommons
We created a new taxonomy of 13 hazard categories, of which 7 have tests in the v0. 5 benchmark.
Can Large Language Models Identify Authorship?
(3) How can LLMs provide explainability in authorship analysis, particularly through the role of linguistic features?
Can Large Language Model Agents Simulate Human Trust Behaviors?
In addition, we probe into the biases in agent trust and the differences in agent trust towards agents and humans.
Can LLM-Generated Misinformation Be Detected?
Then, through extensive empirical investigation, we discover that LLM-generated misinformation can be harder to detect for humans and detectors compared to human-written misinformation with the same semantics, which suggests it can have more deceptive styles and potentially cause more harm.
MetaGAD: Learning to Meta Transfer for Few-shot Graph Anomaly Detection
However, the work exploring limited labeled anomalies and a large amount of unlabeled nodes in graphs to detect anomalies is rather limited.
Combating Health Misinformation in Social Media: Characterization, Detection, Intervention, and Open Issues
Because of the uniqueness and importance of combating health misinformation in social media, we conduct this survey to further facilitate interdisciplinary research on this problem.
When Fairness Meets Privacy: Fair Classification with Semi-Private Sensitive Attributes
Thus, it is essential to ensure fairness in machine learning models.
BOND: Benchmarking Unsupervised Outlier Node Detection on Static Attributed Graphs
To bridge this gap, we present--to the best of our knowledge--the first comprehensive benchmark for unsupervised outlier node detection on static attributed graphs called BOND, with the following highlights.
Fair Classification via Domain Adaptation: A Dual Adversarial Learning Approach
Though we lack the sensitive attribute for training a fair model in the target domain, there might exist a similar domain that has sensitive attributes.
PromptDA: Label-guided Data Augmentation for Prompt-based Few-shot Learners
Extensive experiment results on few-shot text classification tasks demonstrate the superior performance of the proposed framework by effectively leveraging label semantics and data augmentation for natural language understanding.
