Consistent and Distinctive: LLM Benchmark Efficiency via Maximum Independent Set Prompt Selection on Similarity Graphs
arXiv:2606.01400v1 Announce Type: new Abstract: Evaluating large language models (LLMs) across comprehensive benchmarks is expensive and time-consuming. We propose a graph-based prompt selection framework that models each benchmark as a similarity graph -- nodes are prompts connected if their embedding-space distance falls above a configurable threshold -- and applies Maximum Independent Set (MIS) algorithms to select a maximally diverse, non-redundant subset. We evaluate four MIS solvers (CPLEX, GREEDY, Online-MIS, ReduMIS) across six embedding models, three distance measures, six percentile
The proliferation of increasingly complex LLMs and the accompanying need for comprehensive, yet efficient, evaluation benchmarks are driving the development of new testing methodologies.
This development offers a potential solution to the high costs and time associated with LLM benchmarking, crucial for efficient model development and deployment.
The efficiency of LLM evaluation could significantly improve, accelerating research and development cycles and potentially lowering the barrier to entry for new models.
- · AI developers
- · MLOps platforms
- · Cloud providers (cost savings)
- · Academia
- · Inefficient benchmarking services
- · LLM projects with limited evaluation budgets
More efficient and cost-effective evaluation of large language models will become possible.
This improved efficiency could lead to faster iteration and deployment of LLMs, potentially accelerating advancements in AI capabilities.
Reduced benchmarking costs might democratize LLM development, enabling smaller teams or open-source projects to compete more effectively with well-funded entities.
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Read at arXiv cs.CL