arXiv (All)
open access
Efficient Cost-Aware LLM Evaluation via Bayesian Bandit Gittins Indices
Exhaustively evaluating every candidate LLM configuration on every benchmark item to identify a high-performing one is costly. We formulate configuration selection as a cost-aware Bayesian bandit problem and propose GittinsEval, which draws on the Bayesian-optimal Gittins policy to determine which c
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- Cost-Aware Best-LLM Identification using Dueling Feedback#1104559
- IQS-BO: In-Context Query Selection for Bayesian Optimisation#1156541
- Low-Rank Single-Index Bandits with Unknown Links: From Matrices to Tensors#1117737
- Training seeds and model-selection stability in recommender-system evaluation#924376
- Training seeds and model-selection stability in recommender-system evaluation#920145
Record · ID 1061282
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive