Public articles linked to the same research event.
arXiv The work introduces Whole-Pool Setwise reranking, in which each comparison ranks the entire candidate pool, and proposes DualEnd, which jointly selects the candidates predicted to be most and least relevant and fills the ranking from both ends, constructing a complete ranking of 100 candidates in 50 LLM comparisons; with nine open-weight LLMs on TREC DL19 and DL20 it requires 59.4% fewer comparisons than top-oriented windowed Setwise with heapsort and 88.8% fewer than with bubblesort, keeps nDCG@100 within 0.008 of a single-end whole-pool top-oriented approach while roughly halving token consumption and ranking time, and across six BEIR datasets reduces mean token consumption and ranking time by 49.4% and 50.8% relative to that single-end approach.
The work introduces Whole-Pool Setwise reranking, in which each comparison ranks the entire candidate pool, and proposes DualEnd, which jointly selects the candidates predicted to be most and least relevant and fills the ranking from both ends, constructing a complete ranking of 100 candidates in 50 LLM comparisons; with nine open-weight LLMs on TREC DL19 and DL20 it requires 59.4% fewer comparisons than top-oriented windowed Setwise with heapsort and 88.8% fewer than with bubblesort, keeps nDCG@100 within 0.008 of a single-end whole-pool top-oriented approach while roughly halving token consumption and ranking time, and across six BEIR datasets reduces mean token consumption and ranking time by 49.4% and 50.8% relative to that single-end approach.
The work introduces Whole-Pool Setwise reranking, in which each comparison ranks the entire candidate pool, and proposes DualEnd, which jointly selects the candidates predicted to be most and least relevant and fills the ranking from both ends, constructing a complete ranking of 100 candidates in 50 LLM comparisons; with nine open-weight LLMs on TREC DL19 and DL20 it requires 59.4% fewer comparisons than top-oriented windowed Setwise with heapsort and 88.8% fewer than with bubblesort, keeps nDCG@100 within 0.008 of a single-end whole-pool top-oriented approach while roughly halving token consumption and ranking time, and across six BEIR datasets reduces mean token consumption and ranking time by 49.4% and 50.8% relative to that single-end approach.
The work introduces Whole-Pool Setwise reranking, in which each comparison ranks the entire candidate pool, and proposes DualEnd, which jointly selects the candidates predicted to be most and least relevant and fills the ranking from both ends, constructing a complete ranking of 100 candidates in 50 LLM comparisons; with nine open-weight LLMs on TREC DL19 and DL20 it requires 59.4% fewer comparisons than top-oriented windowed Setwise with heapsort and 88.8% fewer than with bubblesort, keeps nDCG@100 within 0.008 of a single-end whole-pool top-oriented approach while roughly halving token consumption and ranking time, and across six BEIR datasets reduces mean token consumption and ranking time by 49.4% and 50.8% relative to that single-end approach.