Training of LLM -Based List-Wise Multilingual Reranker (2025)

Abstract: Multilingual retrieval-augmented generation (MRAG) systems heavily rely on robust Information Retrieval (IR). Reranking as a key component optimizes the initially retrieved document set to present the most pertinent information to the generative model, addressing context limitations and minimizing hallucinations. We propose an approach that trains Large Language Models (LLMs) as multilingual listwise rerankers through supervised fine-tuning (SFT) on a diverse mixture of multilingual and extended English ranking examples, and enhancing reasoning capabilities through Direct Preference Optimization (DPO) from translated task-specific reasoning processes. Experiments demonstrate that the approach improves accuracy@5 by 20-30% across all six high- mediumand low-resource languages compared to the BM25. The posted training 1B models achieve comparable performance to 7B baseline models while enabling faster inference. Finally, we investigate the effectiveness of different reasoning strategies in DPO with crosslingual and monolingual thinking processes.

## introduction large language models (llms) often struggle with factuality, particularly in multilingual contexts with limited training data. rag systems address this by combining llms with external knowledge retrieval, enhancing language performance. in these systems, the ir component is essential, with reranking playing a critical role in refining retrieved documents before decision making ("fusion" stage in figure 1 ). this paper focuses on the rerankers in the multilingual setting, a key component that optimizes retrieved content across diverse languages, ensuring the most relevant information is provided to llms while maintaining efficiency and performance even with limited computational resources. recent advances in reranking have leveraged transformer-based architectures, with llm-based listwise rerankers showing particular promise for reasoning-intensive scenarios. despite these advances, multilingual reranking is underexplored and remains challenging. our contributions are as follows: ## methodology this section will introduce our two-stage training pipeline for developing efficient multilingual rerankers. first, we establish foundational ranking capabilities through sft on a diverse and curated dataset. then, we enhance reasoning-based ranking capabilities using dpo with structured thinking processes. ## experiments evaluation dataset we evaluate reranker models using miracl @xcite , a multilingual information retrieval dataset with queries and relevant passages across 18 languages, focusing on the 6 languages described in ## conclusion our results demonstrate that compact 1b-parameter models can effectively perform multilingual reranking when appropriately trained, with llama-3.2-1b consistently outperforming gemma-3-1b, particularly with diverse training data. the dramatic differences between model families in their ability to incorporate multilingual data highlight the importance of architecture in cross-lingual transfer. for deployment scenarios requiring efficiency across multiple languages, carefully trained 1b models offer an attractive alternative to larger 7b models with comparable performance but faster inference.