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Pre-trained language models have achieved great success in variouslarge-scale information retrieval tasks. However, most of pretraining tasks arebased on counterfeit retrieval data where the query produced by the tailoredrule is assumed as the user's issued query on the given document or passage.Therefore, we explore to use large-scale click logs to pretrain a languagemodel instead of replying on the simulated queries. Specifically, we propose touse user behavior features to pretrain a debiased language model for documentranking. Extensive experiments on Baidu desensitization click logs validate theeffectiveness of our method. Our team on WSDM Cup 2023 Pre-training for WebSearch won the 1st place with a Discounted Cumulative Gain @ 10 (DCG@10) scoreof 12.16525 on the final leaderboard.

Pretraining De-Biased Language Model with Large-scale Click Logs for Document Ranking