Existing approaches typically rely on large-scale fine-tuning to adapt LLMs for information reranking tasks, which is computationally expensive. In this work, we demonstrate that modern LLMs can be effectively adapted using only minimal, high-quality supervision. To enable this, we design LIMRANK-SYNTHESIZER, a reusable and open-source pipeline for generating diverse, challenging, and realistic reranking examples. Using this synthetic data, we fine-tune our reranker model, LIMRANK. We evaluate LIMRANK on two challenging benchmarks, i.e., BRIGHT for reasoning-intensive retrieval and FollowIR for instruction-following retrieval. Our experiments demonstrate that LIMRANK achieves competitive performance, while being trained on less than 5% of the data typically used in prior work. Further ablation studies demonstrate the effectiveness of LIMRANK-SYNTHESIZER and the strong generalization capabilities of LIMRANK across downstream tasks, including scientific literature search and retrieval-augmented generation for knowledge-intensive problem solving.

翻译：现有方法通常依赖大规模微调来使大型语言模型适应信息重排序任务，这需要高昂的计算成本。在本工作中，我们证明现代大型语言模型仅需极少量的高质量监督即可有效适配。为此，我们设计了LIMRANK-SYNTHESIZER——一个可复用且开源的流水线，用于生成多样化、高难度且真实的重排序示例。利用该合成数据，我们对重排序模型LIMRANK进行微调。我们在两个具有挑战性的基准测试上评估LIMRANK，即面向推理密集型检索的BRIGHT和面向指令跟随检索的FollowIR。实验表明，LIMRANK仅使用先前工作中通常所需数据量不足5%的情况下进行训练，即可取得具有竞争力的性能。进一步的消融研究验证了LIMRANK-SYNTHESIZER的有效性，以及LIMRANK在包括科学文献检索和面向知识密集型问题解决的检索增强生成等下游任务中强大的泛化能力。