Are Optimal Algorithms Still Optimal? Rethinking Sorting in LLM-Based Pairwise Ranking with Batching and Caching
This addresses efficiency issues in LLM-based ranking for researchers and practitioners, but it is incremental as it adapts existing sorting concepts to a new cost model.
The paper tackles the problem of sorting algorithms in pairwise ranking prompting (PRP) by shifting the cost model from traditional pairwise comparisons to LLM inferences, revealing that classically optimal algorithms can become inefficient when LLM costs dominate.
We introduce a novel framework for analyzing sorting algorithms in pairwise ranking prompting (PRP), re-centering the cost model around LLM inferences rather than traditional pairwise comparisons. While classical metrics based on comparison counts have traditionally been used to gauge efficiency, our analysis reveals that expensive LLM inferences overturn these predictions; accordingly, our framework encourages strategies such as batching and caching to mitigate inference costs. We show that algorithms optimal in the classical setting can lose efficiency when LLM inferences dominate the cost under certain optimizations.