Large Language Model
Robust Concept Erasure via Kernelized Rate-Distortion Maximization
Distributed representations provide a vector space that captures meaningful relationships between data instances.
Fairness-guided Few-shot Prompting for Large Language Models
However, prior research has shown that in-context learning can suffer from high instability due to variations in training examples, example order, and prompt formats. Therefore, the construction of an appropriate prompt is essential for improving the performance of in-context learning. In this paper, we revisit this problem from the view of predictive bias. Specifically, we introduce a metric to evaluate the predictive bias of a fixed prompt against labels or a given attributes.
Focused Transformer: Contrastive Training for Context Scaling
Large language models have an exceptional capability to incorporate new information in a contextual manner.
CARE-MI: Chinese Benchmark for Misinformation Evaluation in Maternity and Infant Care
While the latest LLMs are astonishingly fluent when interacting with humans, they suffer from the misinformation problem by unintentionally generating factually false statements. This can lead to harmful consequences, especially when produced within sensitive contexts, such as healthcare.