LimRank: Less is More for Reasoning-Intensive Information Reranking
Authors: name: Tingyu Song, name: Yilun Zhao, name: Siyue Zhang, name: Chen Zhao, name: Arman Cohan

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.