Consistent and Distinctive: LLM Benchmark Efficiency via Maximum Independent Set Prompt Selection on Similarity Graphs
Summary
A novel graph-based prompt selection framework enhances the efficiency of large language model (LLM) benchmarking. This method models each benchmark as a similarity graph, where prompts are nodes connected if their embedding-space distance exceeds a configurable threshold. Maximum Independent Set (MIS) algorithms then select a maximally diverse, non-redundant subset of prompts. The framework was evaluated using four MIS solvers, six embedding models, three distance measures, six percentile thresholds, and four benchmarks (GPQA, IFEval, MMLU-Pro, Omni-MATH) across 66 LLMs. Results confirm consistent LLM rankings, with Kendall's W ≥ 0.90 in 99.2% of stochastic configurations (mean W = 0.997 ± 0.008). The approach achieves 25-48% prompt reduction on average at higher percentile thresholds. Ranking divergence from the full benchmark (ρ< 0.95) occurred in only 15.95% of configurations, primarily at low thresholds (p₁₀--p₂₀) and for GPQA and IFEval benchmarks, identifying overly dense graphs as the main failure mode.
Key takeaway
For MLOps Engineers or AI Scientists managing large language model evaluations, this graph-based prompt selection framework offers a critical efficiency gain. You can achieve 25-48% prompt reduction, significantly cutting evaluation time and computational costs, while maintaining highly consistent LLM rankings. Implement this Maximum Independent Set approach to optimize your benchmarking processes, especially for benchmarks like MMLU-Pro and Omni-MATH, where ranking divergence is minimal.
Key insights
A graph-based Maximum Independent Set (MIS) approach efficiently selects diverse LLM benchmark prompts, maintaining ranking consistency.
Principles
- Similarity graphs can model prompt redundancy.
- MIS algorithms identify maximally diverse prompt subsets.
Method
Model benchmarks as similarity graphs where prompts are nodes and edges connect similar prompts; apply MIS algorithms to select a diverse subset.
In practice
- Reduce LLM evaluation costs by 25-48%.
- Identify benchmarks prone to ranking divergence at low thresholds.
Topics
- LLM Benchmarking
- Prompt Selection
- Graph Algorithms
- Maximum Independent Set
- Model Evaluation
- Computational Efficiency
Best for: Research Scientist, AI Engineer, NLP Engineer, AI Scientist, Machine Learning Engineer, MLOps Engineer
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Editorial summary, takeaway, and curation by AIssential. Original article published by Artificial Intelligence.