IR2: Information Regularization for Information Retrieval
<|reference_start|>IR2: Information Regularization for Information Retrieval: Effective information retrieval (IR) in settings with limited training data, particularly for complex queries, remains a challenging task. This paper introduces IR2, Information Regularization for Information Retrieval, a technique for reducing overfitting during synthetic data generation. This approach, representing a novel application of regularization techniques in synthetic data creation for IR, is tested on three recent IR tasks characterized by complex queries: DORIS-MAE, ArguAna, and WhatsThatBook. Experimental results indicate that our regularization techniques not only outperform previous synthetic query generation methods on the tasks considered but also reduce cost by up to 50%. Furthermore, this paper categorizes and explores three regularization methods at different stages of the query synthesis pipeline-input, prompt, and output-each offering varying degrees of performance improvement compared to models where no regularization is applied. This provides a systematic approach for optimizing synthetic data generation in data-limited, complex-query IR scenarios. All code, prompts and synthetic data are available at https://github.com/Info-Regularization/Information-Regularization.<|reference_end|>
@article{wang2024ir2:,
    title={IR2: Information Regularization for Information Retrieval},
    author={Jianyou Wang, Kaicheng Wang, Xiaoyue Wang, Weili Cao, Ramamohan
  Paturi, Leon Bergen},
    journal={arXiv preprint arXiv:2402.16200},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.16200},
    primaryClass={cs.IR cs.AI cs.CL cs.LG}
}