arXiv (All)
open access
CARE: Confounder-Aware Aggregation for Reliable LLM Evaluation
LLM-as-a-judge ensembles are the standard paradigm for scalable evaluation, but their aggregation mechanisms suffer from a fundamental flaw: they implicitly assume that judges provide independent estimates of true quality. However, in practice, LLM judges exhibit correlated errors caused by shared l
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
- Benchmarking LLM-as-a-Judge for Long-Form Output Evaluation#684480
- Benchmarking LLM-as-a-Judge for Long-Form Output Evaluation#682705
- When Accuracy Gaps Fail to Certify: Auditing Cross-Domain Recalibration of LLM Judges#1064498
- From IR to RecSys: Evaluating LLM-based Judges in Cranfield-style Recommendation Collections#617902
- The Lifecycle of LLM-as-a-Judge for Large-Scale Recommendation Explanations#819452
Record · ID 986205
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