Incremental Pooled LLM Evaluation for Cost-Effective Retrieval Model Selection
Selecting a retrieval model for a production RAG system requires reliable comparative evaluation, but obtaining relevance judgments at scale is expensive and difficult to repeat as new candidate systems arrive. We study pooled LLM evaluation, in which an LLM judges the union of documents retrieved by the current set of candidate systems, and the pool is then expanded incrementally as new systems are introduced by judging only the new documents they contribute. These judgments are reused to evaluate all systems on a common basis. We validate this approach on four retrieval benchmarks with 11 systems spanning dense, sparse, and hybrid configurations, and deploy it to compare 62 retrieval co...
| Authors | Max Nelson, Hanoz Bhathena, Aviral Joshi et al. |
|---|---|
| Primary category | cs.IR |
| Published | 2 September 2026 |
| Last updated at source | 2 September 2026 |
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