QPP-RA: Aggregating Large Language Model Rankings
Filippo Betello, Matteo Russo, Paul Dütting, Stefano Leonardi, Fabrizio Silvestri · 2025
Recent advances in Large Language Models (LLMs) have significantly improved search and recommendation systems. In the field of information retrieval, LLMs are increasingly used as re-rankers to refine the relevance of documents initially retrieved by search algorithms. It is also a common strategy to apply ranking aggregation techniques to improve the performance of the search algorithm. Existing aggregation methods, such as Borda Count and Reciprocal Rank Fusion (RRF), rely exclusively on the positional information of the retrieved documents and do not take into account the different performance levels of the different models, potentially degrading the overall effectiveness.