Fairness-guided Few-shot Prompting for Large Language Models
- URL: http://arxiv.org/abs/2303.13217v3
- Date: Fri, 31 Mar 2023 06:11:06 GMT
- Title: Fairness-guided Few-shot Prompting for Large Language Models
- Authors: Huan Ma, Changqing Zhang, Yatao Bian, Lemao Liu, Zhirui Zhang, Peilin
Zhao, Shu Zhang, Huazhu Fu, Qinghua Hu, Bingzhe Wu
- Abstract summary: In-context learning can suffer from high instability due to variations in training examples, example order, and prompt formats.
We introduce a metric to evaluate the predictive bias of a fixed prompt against labels or a given attributes.
We propose a novel search strategy based on the greedy search to identify the near-optimal prompt for improving the performance of in-context learning.
- Score: 93.05624064699965
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: 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. Specifically, we introduce a metric to evaluate the predictive bias of a
fixed prompt against labels or a given attributes. Then we empirically show
that prompts with higher bias always lead to unsatisfactory predictive quality.
Based on this observation, we propose a novel search strategy based on the
greedy search to identify the near-optimal prompt for improving the performance
of in-context learning. We perform comprehensive experiments with
state-of-the-art mainstream models such as GPT-3 on various downstream tasks.
Our results indicate that our method can enhance the model's in-context
learning performance in an effective and interpretable manner.
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