Abstract: Cross-lingual Information Retrieval(CLIR) is an important information acquisition task in natural language proces-sing.Recently,LLM-based retrieval methods have gained attention and demonstrated remarkable progress in this task.However,existing unsupervised retrieval methods based on prompting large language models still insufficient in effectiveness and efficiency.To solve this problem,this paper introduces a novel CLIR method based on aligned query.Specifically,this paper adopts the “pretrain-finetune” paradigm and proposes an adaptive self-teaching encoder based on a pretrained multilingual model to guide cross-lingual retrieval learning by mono-lingual retrieval learning.This method introduces semantically aligned queries in the same language as the documents and designs an adaptive self-teaching mechanism to guide cross-lingual retrieval by leveraging the probability distribution of mono-lingual retrieval results from different linguistic perspectives.To evaluate the effectiveness and efficiency of this method,this paper conducts extensive experiments on 22 language pairs.The results demonstrate that the proposed method achieves SOTA performance in terms of MRR.In particular,this method improves average MRR by 15.45% over the sub-optimal baseline in high-resource language pairs and 18.9% over the sub-optimal baseline in low-resource language pairs.Furthermore,the method reduces training and inference times compared to LLM-based approaches and exhibits faster convergence with enhanced stability.

Key words: Cross-lingual Information Retrieval, Aligned query, Self-teaching, Adaptive layer-wise coefficient