Fairness-guided Few-shot Prompting for Large Language Models

Understand

Large language models have demonstrated surprising ability to perform in-context learning, i.e., these models can be directly applied to solve numerous downstream tasks by conditioning on a prompt constructed by a few input-output examples.

• 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.