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LimRank: Less is More for Reasoning-Intensive Information Reranking
Tingyu Song, Yilun Zhao, Siyue Zhang +2
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 m…
Efficiency-Effectiveness Reranking FLOPs for LLM-based Rerankers
Zhiyuan Peng, Ting-ruen Wei, Tingyu Song +1
Large Language Models (LLMs) have recently been applied to reranking tasks in information retrieval, achieving strong performance. However, their high computational demands often h…
IFIR: A Comprehensive Benchmark for Evaluating Instruction-Following in Expert-Domain Information Retrieval
Tingyu Song, Guo Gan, Mingsheng Shang +1
We introduce IFIR, the first comprehensive benchmark designed to evaluate instruction-following information retrieval (IR) in expert domains. IFIR includes 2,426 high-quality examp…
Uncertainty of Thoughts: Uncertainty-Aware Planning Enhances Information Seeking in Large Language Models
Zhiyuan Hu, Chumin Liu, Xidong Feng +6
In the face of uncertainty, the ability to *seek information* is of fundamental importance. In many practical applications, such as medical diagnosis and troubleshooting, the infor…