Reinforcement Learning for Effective Few-Shot Ranking

Reinforcement Learning for Effective Few-Shot Ranking

연구 분야: Artificial Intelligence

학회: SIGIR '25: Proceedings of the 48th International ACM SIGIR Conference on Research and Development in Information Retrieval

Neural rankers have achieved strong retrieval effectiveness but require large amounts of labeled data, limiting their applicability in few-shot settings. In this paper, we address the sample inefficiency of neural ranking methods by introducing a Reinforcement Learning (RL)-based re-ranking model that achieves high effectiveness with minimal training data. Built on a Deep Q-learning Network (DQN) framework, our approach is designed for few-shot settings, maximizing sample efficiency to ensure robust generalization from limited interactions. Extensive experiments show that our model significantly outperforms data-intensive methods and existing few-shot baselines, demonstrating RL's potential to enhance IR capabilities in few-shot scenarios.