{"paper":{"title":"Dynamic Ranked List Truncation for Reranking Pipelines via LLM-generated Reference-Documents","license":"http://creativecommons.org/licenses/by/4.0/","headline":"LLM-generated reference documents serve as pivots to dynamically truncate ranked lists and accelerate listwise reranking.","cross_cats":[],"primary_cat":"cs.IR","authors_text":"Debasis Ganguly, Nilanjan Sinhababu, Pabitra Mitra, Soumedhik Bharati","submitted_at":"2026-04-10T16:59:54Z","abstract_excerpt":"Large Language Models (LLM) have been widely used in reranking. Computational overhead and large context lengths remain a challenging issue for LLM rerankers. Efficient reranking usually involves selecting a subset of the ranked list from the first stage, known as ranked list truncation (RLT). The truncated list is processed further by a reranker. For LLM rerankers, the ranked list is often partitioned and processed sequentially in batches to reduce the context length. Both these steps involve hyperparameters and topic-agnostic heuristics. Recently, LLMs have been shown to be effective for rel"},"claims":{"count":4,"items":[{"kind":"strongest_claim","text":"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.","source":"verdict.strongest_claim","status":"machine_extracted","claim_id":"C1","attestation":"unclaimed"},{"kind":"weakest_assumption","text":"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.","source":"verdict.weakest_assumption","status":"machine_extracted","claim_id":"C2","attestation":"unclaimed"},{"kind":"one_line_summary","text":"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.","source":"verdict.one_line_summary","status":"machine_extracted","claim_id":"C3","attestation":"unclaimed"},{"kind":"headline","text":"LLM-generated reference documents serve as pivots to dynamically truncate ranked lists and accelerate listwise reranking.","source":"verdict.pith_extraction.headline","status":"machine_extracted","claim_id":"C4","attestation":"unclaimed"}],"snapshot_sha256":"fd6a2866d0e851eaca05281cf3f5e4624f9374e5d36346df916b410eb6554f03"},"source":{"id":"2604.09492","kind":"arxiv","version":2},"verdict":{"id":"fc6ccfb3-af16-4963-b18c-3044ef4afd77","model_set":{"reader":"grok-4.3"},"created_at":"2026-05-10T16:22:51.626003Z","strongest_claim":"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.","one_line_summary":"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.","pipeline_version":"pith-pipeline@v0.9.0","weakest_assumption":"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.","pith_extraction_headline":"LLM-generated reference documents serve as pivots to dynamically truncate ranked lists and accelerate listwise reranking."},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2604.09492/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"}