pith:QGX67E2P
Dynamic Ranked List Truncation for Reranking Pipelines via LLM-generated Reference-Documents
LLM-generated reference documents serve as pivots to dynamically truncate ranked lists and accelerate listwise reranking.
arxiv:2604.09492 v2 · 2026-04-10 · cs.IR
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Claims
Experiments on TREC Deep Learning benchmarks show that our approach outperforms existing RLT-based approaches. In-domain and out-of-domain benchmarks demonstrate that our proposed methods accelerate LLM-based listwise reranking by up to 66% compared to existing approaches.
LLMs can be used to generate reference documents that act as a reliable pivot between relevant and non-relevant documents in a ranked list, based on the equivalence to relevance judgment.
LLM-generated reference documents enable dynamic ranked list truncation and adaptive batching for listwise reranking, outperforming prior RLT methods and accelerating processing by up to 66% on TREC benchmarks.
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| First computed | 2026-05-29T01:05:09.184570Z |
|---|---|
| Builder | pith-number-builder-2026-05-17-v1 |
| Signature | Pith Ed25519
(pith-v1-2026-05) · public key |
| Schema | pith-number/v1.0 |
Canonical hash
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curl -sH 'Accept: application/ld+json' https://pith.science/pith/QGX67E2PZKBZW3VIO7CXPJO4WN \
| jq -c '.canonical_record' \
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Canonical record JSON
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