Task-Aware Reduction for Scalable LLM-Database Systems

2025-10-13

AI Summary

Large language models (LLMs) face significant challenges in data-intensive tasks—such as database querying and developer observability—including excessive input length, high noise, and prohibitive token consumption. Method: This paper formalizes the token budget as a finite attention resource and introduces *task-aware input text reduction* as a first-class design principle in language-data systems. We propose an adaptive text reduction pipeline that jointly leverages task semantics and token-budget awareness during preprocessing, enabling goal-directed information preservation—not generic compression. Contribution/Results: Experiments demonstrate substantial reductions in computational overhead and carbon footprint, alongside improved query accuracy and system scalability. Our approach establishes a new paradigm for sustainable, high-performance LLM–database co-processing in noisy, large-scale data environments.

Technology Category

• Natural Language Processing: (Large) Language Models
• Machine Learning: Large Multimodal Models (LMMs)
• Data Mining & Knowledge Management: Intelligent Query Processing

Application Category

• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Search and Retrieval-Augmented AI: Large language models for search

Abstract

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.

Problem

• Reducing verbose text inputs for cost-effective LLM-database integration
• Optimizing token allocation to prioritize task-relevant information
• Addressing scalability challenges in noisy data-intensive LLM workflows

Innovation

• Task-aware text reduction optimizes LLM attention allocation
• Input-side reduction prioritizes task-relevant information signals
• Token-budget-aware preprocessing enables sustainable data integration