A deployable multilingual reranker must not only generalize across languages, domains, and ranking tasks, but also remain efficient to serve as a second-stage reranker in practical systems. However, adapting it to new target distributions typically requires extensive task-specific relevance annotations. We present Querit-Reranker, a family of multilingual rerankers trained with a data-centric pipeline for label-efficient adaptation. We instantiate it as Querit-Reranker-A0.4B, initialized from an in-house MoE backbone with 0.4B activated parameters, and Querit-Reranker-4B, initialized from Qwen3-Embedding-4B. Our pipeline first learns general relevance modeling from large-scale ranking-oriented data, then adapts to target distributions through synthetic-query mining with teacher scores as continuous soft labels. To consolidate complementary task-adapted strengths, we further merge checkpoints via spherical linear interpolation, obtaining a single deployable model without runtime ensembling overhead. Using Qwen3-Embedding-0.6B as the first-stage retriever, Querit-Reranker-A0.4B improves average nDCG@10 from 54.11 to 59.28 on BEIR and from 59.87 to 67.70 on MIRACL. On MTEB Multilingual v2 Reranking, it also shows competitive performance with larger embedding-based models, while Querit-Reranker-4B further achieves state-of-the-art performance among all evaluated models. We release both models on Hugging Face.

“本文介绍了我们在第二十五届中国计算语言学大会(CCL 2025)中文叙实性推理评测(FIE2025)中荣获双赛道第一名和第二名的系统方案。针对中文叙实性推理任务中模型需要从谓词语义正确推断事件真实性的挑战,我们提出了层次化思维链(Hierarchical Chain-of-Thought, HCoT)推理框架,通过结构化的多级推理过程引导模型逐步识别关键谓词、分析其叙实性类型及其在否定、疑问等复杂语境下的叙实性变化。在非微调赛道中,我们通过集成多种强大的推理型大模型(如Deepseek-R1-671B、Deepseek-v3-671B、GPT-4o、Gemini-2.5-pro-0506等)的预测结果,并采用自适应投票策略,取得了0.9376的分数。在微调赛道上,我们构建了高质量的思维链指令数据集,发现专注于推理能力的基础模型(如DeepSeek-R1-Distill-Qwen-32B)经微调后在叙实性推理任务上优于同等规模甚至更大参数量的通用大模型(如Qwen2.5-72B-Instruct)。通过伪标签训练进一步优化,最终在官方评测中取得0.9396的最高正确率。实验结果表明,我们提出的层次化思维链结构与推理模型的结合在中文叙实性推理任务中具有显著优势,特别是在处理复杂语境和隐含语义的情况下。”