Skip to content
NewA calmer, faster ZeroHour
AI researchmedium

Pinocchio: Fast Uncertainty Estimates for Black-Box Language Models

arXiv cs.AI / cs.LG / cs.CLby Kevin David Hayes ()1 min read
Read at arxiv.org
AI summary · glm-5.3Summaries by Vani AI

Pinocchio introduces an external calibrator for black-box LLM API models with 0.862 AUROC and zero-shot transfer to thirteen unseen models.

This paper introduces Pinocchio, an external calibrator for black-box LLM API models, achieving 0.862 AUROC and zero-shot transfer across thirteen unseen models.

Key facts
  • Pinocchio is an external calibrator for black-box LLM API models.
  • It achieves 0.862 AUROC with zero-shot transfer to thirteen unseen models.
  • The model uses only a single forward pass and requires no access to model logits or weights.
Explain this AI news with Vani AIWhat was announced, what is new and why it matters, in a new chat
Tags
ai-modelblack-boxcalibrationlanguage-modeluncertainty
Organizations
Thirteen Unseen Models
AI models
Pinocchio

From the original article

1 min read at arxiv.org

In high-stakes decision-making applications of large language models (LLMs), practitioners require not only accurate LLMs but also uncertainty estimates for their predictions. Existing approaches to uncertainty estimation for LLMs require access to log-probabilities output by the model or require fine-tuning access.

arXiv cs.AI / cs.LG / cs.CL

By Kevin David Hayes · arxiv.org

Read the full article at arXiv cs.AI / cs.LG / cs.CL

A short excerpt; the article and its images belong to the publisher. Summary, key facts and assessment above are AI-written from the article and can be wrong.