Alper unifies entity resolution matching and clustering into an iterative graph refinement and probabilistic label propagation process that adaptively selects LLM queries via a budgeted greedy optimization to outperform cascaded pipelines on eight benchmarks.
Thriftllm: On cost-effective selection of large language models for classification queries
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A governed LLM routing system for lab tutoring raises challenge-alignment from 0.90 to 0.98, boosts productive-struggle time, and cuts token costs by two-thirds while preserving answer accuracy.
HoldUp uses LLM-guided clustering to provide holistic dataset context for semantic operators, yielding up to 33% higher classification accuracy and 30% higher scoring accuracy than row-by-row LLM processing across 15 datasets.
Develops a constrained bandit algorithm for online LLM selection under packing and covering constraints with time-varying demand, claiming sublinear regret and constraint violations versus an offline full-information benchmark.
citing papers explorer
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Adaptive Graph Refinement and Label Propagation with LLMs for Cost-Effective Entity Resolution
Alper unifies entity resolution matching and clustering into an iterative graph refinement and probabilistic label propagation process that adaptively selects LLM queries via a budgeted greedy optimization to outperform cascaded pipelines on eight benchmarks.
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Policy-Governed LLM Routing with Intent Matching for Instrument Laboratories
A governed LLM routing system for lab tutoring raises challenge-alignment from 0.90 to 0.98, boosts productive-struggle time, and cuts token costs by two-thirds while preserving answer accuracy.
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Semantic Data Processing with Holistic Data Understanding
HoldUp uses LLM-guided clustering to provide holistic dataset context for semantic operators, yielding up to 33% higher classification accuracy and 30% higher scoring accuracy than row-by-row LLM processing across 15 datasets.
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Online LLM Selection via Constrained Bandits with Time-Varying Demand
Develops a constrained bandit algorithm for online LLM selection under packing and covering constraints with time-varying demand, claiming sublinear regret and constraint violations versus an offline full-information benchmark.