aiqbench analysis
This article is a systems evaluation of whether System-1-style decision models can replace dedicated rerankers in retrieval-augmented generation. Its central findings are cautionary: default setups fail on long candidate sets, and a tuned reranker remains faster and far more memory-efficient, so the answer is "sometimes, under specific conditions." For ML practitioners, the practical takeaway is to tune the existing reranker before considering a swap.
Can System-1-Style Decision Models Replace Dedicated Rerankers in RAG? A Systems Evaluation
arXiv:2610.34816v1 Announce Type: new Abstract: Retrieval-augmented generation (RAG) systems often use a dedicated reranker to choose which retrieved candidates reach the language model. Reranking improves relevance but adds latency, so teams look for faster alternatives. System-1style decision models make direct, structured predictions instead of generating long responses. Could one provide comparable reranking quality with less inference overhead? We first screen 21 open-weight models on 11 benchmarks, then compare a promising decision model with a dedicated reranker on identical candidate lists. The default decision-model setup fails on many long candidate sets, so scoring only the requests it completes gives a misleading ranking. Limiting candidate text length largely restores reliability and makes some configurations competitive. A frozen evaluation on new retrieval data supports the reliability and ranking results, but confirms the latency requirement for only one of the two selected configurations. Across workloads, which model ranks better changes, and a tuned reranker remains faster and uses far less GPU memory. A decision model can perform competitive reranking, but replacing a reranker requires weighing quality, latency and memory together, and teams should tune the reranker they already run first.
Title:Can System-1-Style Decision Models Replace Dedicated Rerankers in RAG? A Systems Evaluation
Abstract:Retrieval-augmented generation (RAG) systems often use a dedicated reranker to choose which retrieved candidates reach the language model. Reranking improves relevance but adds latency, so teams look for faster alternatives. System-1style decision models make direct, structured predictions instead of generating long responses. Could one provide comparable reranking quality with less inference overhead? We first screen 21 open-weight models on 11 benchmarks, then compare a promising decision model with a dedicated reranker on identical candidate lists. The default decision-model setup fails on many long candidate sets, so scoring only the requests it completes gives a misleading ranking. Limiting candidate text length largely restores reliability and makes some configurations competitive. A frozen evaluation on new retrieval data supports the reliability and ranking results, but confirms the latency requirement for only one of the two selected configurations. Across workloads, which model ranks better changes, and a tuned reranker remains faster and uses far less GPU memory. A decision model can perform competitive reranking, but replacing a reranker requires weighing quality, latency and memory together, and teams should tune the reranker they already run first.
Machine Learning (cs.LG)
arXiv:2610.34816 [cs.LG]
(or arXiv:2610.34816v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2610.34816 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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Full text under the publisher's open licence · 287 words