Where Do LLM Ensemble Gains Come From? Decomposing Candidate Value, Diversity, and Aggregation

Title:Where Do LLM Ensemble Gains Come From? Decomposing Candidate Value, Diversity, and Aggregation

Abstract:LLM ensemble gains are often measured against a single baseline, but the value attributed to additional candidates can change with the comparison used. We study an LLM aggregator under four matched conditions: Direct (no candidates), Single-slot (one candidate in a multi-slot interface), Duplicate (one candidate repeated), and Multi-candidate (separately generated candidates). Under either non-diverse control, overall candidate gain (CIV) splits algebraically into an additional-candidate contrast (DSV) and a candidate-context contrast (CCV); the split, but not CIV, depends on the chosen control. On MultiHopRAG model diversity (k=2), the repetition gap Rep=-8.33 pp [-13.25, -3.75] quantifies the difference between gains measured against Duplicate and Single-slot. On a matched FRAMES stronger-panel extension (n=200, k=3, aggregator output limit 4096), paired Delta Rep=+9.67 pp [+6.00, +13.50]; within panels, Rep changes sign (original -6.33; stronger +3.33). In FRAMES evidence aggregation, candidate-context contrasts remain strongly negative under both non-diverse controls. Our results show why ensemble evaluations should state the reference control before attributing gains to additional candidates.

Computation and Language (cs.CL); Artificial Intelligence (cs.AI)

arXiv:2610.18786 [cs.CL]

(or arXiv:2610.18786v1 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2610.18786 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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