Verbalizing LLM's Higher-order Uncertainty via Imprecise Probabilities
Verbalizing LLM's Higher-order Uncertainty via Imprecise Probabilities explores A novel approach to improve uncertainty elicitation from large language models using imprecise probabilities.. Commercial viability score: 4/10 in NLP.
BUILDER'S SANDBOX
Build This Paper
Use an AI coding agent to implement this research.
OpenAI CodexAI Agent
Lightweight coding agent in your terminal.
Claude CodeAI Agent
Agentic coding tool for terminal workflows.
AntiGravity IDEScaffolding
AI agent mindset installer and workflow scaffolder.
CursorIDE
AI-first code editor built on VS Code.
VS CodeIDE
Free, open-source editor by Microsoft.
Recommended Stack
Startup Essentials
Estimated $9K - $13K over 6-10 weeks.
See exactly what it costs to build this -- with 3 comparable funded startups.
7-day free trial. Cancel anytime.
Discover the researchers behind this paper and find similar experts.
7-day free trial. Cancel anytime.
Related Papers
- Builds On ThisFrom Entropy to Calibrated Uncertainty: Training Language Models to Reason About UncertaintyViability: 3.0
- Builds On ThisTowards Reducible Uncertainty Modeling for Reliable Large Language Model AgentsViability: 3.0
- Prior WorkThe Anatomy of Uncertainty in LLMsViability: 4.0
- Prior WorkA Formal Framework for Uncertainty Analysis of Text Generation with Large Language ModelsViability: 4.0
- Higher ViabilityHow Uncertain Is the Grade? A Benchmark of Uncertainty Metrics for LLM-Based Automatic Assessment
Related Resources
- How does PersianPunc contribute to NLP?(question)
- How does PersianPunc contribute to NLP?(question)
- How does PersianPunc contribute to NLP?(question)
- NLP – Use Cases(use_case)