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About this paper
Sheshansh Agrawal, Thien Hang Nguyen, Douwe Kiela
A principled zero-shot ranking framework, BLITZRANK, is presented that utilizes tournament graphs for efficiently identifying top-m items from n candidates via k-wise comparison queries. This approach fully leverages k-wise information and robustly handles non-transitive preferences, achieving comparable or superior ranking quality with 25-40% fewer LLM tokens than existing listwise methods in document reranking.
Key insights
- Fully extracting and aggregating all pairwise preferences from k-wise comparisons via transitive closure dramatically improves query efficiency compared to methods that discard information.
- Non-transitive preferences generated by LLMs or human judges often indicate genuine ambiguity or equivalence among items rather than mere noise, and can be structurally incorporated into a principled tiered ranking.
- A rigorous graph-theoretic foundation with explicit correctness and termination guarantees can provide a robust and transparent approach to comparison-based ranking.
Summary by alphaXiv.
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