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
this https URL

使用 IR2 信息规范化技术在有限训练数据环境下，针对复杂查询进行综合数据生成和信息检索，实验结果表明这种规范化技术在数据生成过程中优于以往的方法，并可将成本降低 50%。