Semantic Operators and Their Optimization: Towards AI-Based Data Analytics with Accuracy Guarantees

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Abstract

The semantic capabilities of large language models (LLMs) have the potential to enable rich analytics and reasoning over vast knowledge corpora. Unfortunately, existing systems either empirically optimize expensive LLM-powered operations with no performance guarantees , or limit their support to simple batched-inference primitives. We introduce semantic operators , the rst formalism with statistical accuracy guarantees for general-purpose AI-based operations with natural language parameters (e.g., ltering, sorting, joining or aggregating records using natural language criteria). Each operator can be implemented by multiple AI algorithms , which compose individual model invocations to orchestrate the model over the data. Our programming model species the expected behavior of each operator with a high-quality reference algorithm , and we develop an optimization framework that reduces cost, while providing accuracy guarantees for individual operators. Using this approach, we propose several novel optimizations to accelerate semantic ltering, joining, group-by and top-k operations by up to 1 , 000 ⇥ . We implement semantic operators in the LOTUS system and demonstrate LOTUS’ eectiveness on real, bulk-semantic processing applications, including fact-checking, biomedical multilabel classication, search, and topic analysis. We show that the semantic operator model is expressive, capturing state-of-the-art AI pipelines in a few operator calls, and making it easy to express new pipelines that match or exceed quality of recent LLM-based analytic systems by up to 170%, while oering accuracy guarantees. Overall, LOTUS programs match or exceed the accuracy of state-ofthe-art AI pipelines for each task while running up to 3 . 6 ⇥ faster than the highest-quality baselines. LOTUS is publicly available at https://github.com/lotus-data/lotus.