Language-Guided Reinforcement Learning for Hard Attention in Few-Shot Learning

TL;DR

This paper introduces LaHA, a language-guided reinforcement learning framework that enhances few-shot learning by focusing on critical data segments, improving interpretability and accuracy.

Contribution

The paper presents a novel language-guided deep reinforcement learning approach for hard attention in few-shot learning, addressing data scarcity and model complexity.

Findings

LaHA improves accuracy on benchmark datasets.

LaHA enhances interpretability of models.

LaHA outperforms existing attention methods.

Abstract

Attention mechanisms have demonstrated significant potential in enhancing learning models by identifying key portions of input data, particularly in scenarios with limited training samples. Inspired by human perception, we propose that focusing on essential data segments, rather than the entire dataset, can improve the accuracy and reliability of the learning models. However, identifying these critical data segments, or "hard attention finding," is challenging, especially in few-shot learning, due to the scarcity of training data and the complexity of model parameters. To address this, we introduce LaHA, a novel framework that leverages language-guided deep reinforcement learning to identify and utilize informative data regions, thereby improving both interpretability and performance. Extensive experiments on benchmark datasets validate the effectiveness of LaHA.