Task-Aware Reduction for Scalable LLM-Database Systems
This addresses the challenge of making LLM-based data workflows more efficient and aligned with tasks, though it is incremental as it builds on existing efficiency efforts by focusing on input-side reduction.
The paper tackles the problem of high costs and inefficiency in LLM-database systems due to large, noisy text data by proposing task-aware text reduction as a design principle to allocate attention resources effectively, aiming to enable scalable and sustainable integration.
Large Language Models (LLMs) are increasingly applied to data-intensive workflows, from database querying to developer observability. Yet the effectiveness of these systems is constrained by the volume, verbosity, and noise of real-world text-rich data such as logs, telemetry, and monitoring streams. Feeding such data directly into LLMs is costly, environmentally unsustainable, and often misaligned with task objectives. Parallel efforts in LLM efficiency have focused on model- or architecture-level optimizations, but the challenge of reducing upstream input verbosity remains underexplored. In this paper, we argue for treating the token budget of an LLM as an attention budget and elevating task-aware text reduction as a first-class design principle for language -- data systems. We position input-side reduction not as compression, but as attention allocation: prioritizing information most relevant to downstream tasks. We outline open research challenges for building benchmarks, designing adaptive reduction pipelines, and integrating token-budget--aware preprocessing into database and retrieval systems. Our vision is to channel scarce attention resources toward meaningful signals in noisy, data-intensive workflows, enabling scalable, accurate, and sustainable LLM--data integration.