[2603.04799] Beyond Linear LLM Invocation: An Efficient and Effective Semantic Filter Paradigm

Computer Science > Databases arXiv:2603.04799 (cs) [Submitted on 5 Mar 2026] Title:Beyond Linear LLM Invocation: An Efficient and Effective Semantic Filter Paradigm Authors:Nan Hou, Kangfei Zhao, Jiadong Xie, Jeffrey Xu Yu View a PDF of the paper titled Beyond Linear LLM Invocation: An Efficient and Effective Semantic Filter Paradigm, by Nan Hou and 3 other authors View PDF HTML (experimental) Abstract:Large language models (LLMs) are increasingly used for semantic query processing over large corpora. A set of semantic operators derived from relational algebra has been proposed to provide a unified interface for expressing such queries, among which the semantic filter operator serves as a cornerstone. Given a table T with a natural language predicate e, for each tuple in the relation, the execution of a semantic filter proceeds by constructing an input prompt that combines the predicate e with its content, querying the LLM, and obtaining the binary decision. However, this tuple-by-tuple evaluation necessitates a complete linear scan of the table, incurring prohibitive latency and token costs. Although recent work has attempted to optimize semantic filtering, it still does not break the linear LLM invocation barriers. To address this, we propose Clustering-Sampling-Voting (CSV), a new framework that reduces LLM invocations to sublinear complexity while providing error guarantees. CSV embeds tuples into semantic clusters, samples a small subset for LLM evaluation, and infers...