Computational · Paper · 2026

Recovering LLM-Persona Accuracies from Unlabeled Votes

Zenodo

Catalog Row152
Citation KeyFriedman2026RecoveringLLMPersonaAccuracies152
Paper FolderAvailable
DOI10.5281/zenodo.20498699
Platform availability

Overview

Extracted from the local paper documentation when available.

Algebraic (NTQR) evaluation infers how accurate a group of noisy classifiers was on a finite test using only their responses — no answer key. We test this end to end on real large language models. Three trader "personas" (optimistic, neutral, pessimistic), instantiated as system prompts, each make a binary bullish/bearish call on the same 64 market scenarios; we run the identical trio thr...

algebraic evaluationNTQRunsupervised evaluationevaluation on unlabeled dataLLM-as-judgeerror-independent evaluationensemble evaluabilityconstant classifierAI safety warning lightreproducible researchanswer-key-free recoverylocal large language models

Use Notes

Concise findings and methods pulled from README/SKILL documentation.

Findings / Concepts
  • Algebraic (NTQR) evaluation infers how accurate a group of noisy classifiers was on a finite test using only their responses — no answer key.
  • We test this end to end on real large language models.
Methods / Techniques
  • Software pipeline design
  • Data-driven analysis

Citation

Plain-text citation for quick reuse.

Friedman, Daniel Ari. 2026. Recovering LLM-Persona Accuracies from Unlabeled Votes. Zenodo. DOI: 10.5281/zenodo.20498699. URL: https://doi.org/10.5281/zenodo.20498699.

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