# Perturbation-based Active Learning for Question Answering

Source: [Perturbation-based Active Learning for Question Answering](<https://www.sota2.com/research/paper/68fa710f1e209131cea4ef9d>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-26T01:57:41.618Z

Authors: Fan Luo, Mihai Surdeanu

Published: 2023-11-04T08:07:23.000Z

## Abstract

Building a question answering (QA) model with less annotation costs can be achieved by utilizing active learning (AL) training strategy. It selects the most informative unlabeled training data to update the model effectively. Acquisition functions for AL are used to determine how informative each training example is, such as uncertainty or diversity based sampling. In this work, we propose a perturbation-based active learning acquisition strategy and demonstrate it is more effective than existing commonly used strategies.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2311.02345>)

[PDF](<https://arxiv.org/pdf/2311.02345.pdf>)