BriefGPT.xyz
May, 2023
探索合成查询生成在相关性预测中的可行性
Exploring the Viability of Synthetic Query Generation for Relevance Prediction
HTML
PDF
Aditi Chaudhary, Karthik Raman, Krishna Srinivasan, Kazuma Hashimoto, Mike Bendersky...
TL;DR
本研究探讨了如何利用 QGen 方法进行细粒度的相关性预测,介绍了 label-conditioned QGen 模型来区分不同等级的相关性,然而 QGen 方法仍然难以捕捉到完整的相关性标签空间,从而生成的查询与所需的相关性标签不符。
Abstract
query-document relevance
prediction is a critical problem in Information Retrieval systems. This problem has increasingly been tackled using (pretrained)
transformer-based models
which are finetuned using large c
→