AI RESEARCH
Textual Bayes: Quantifying Prompt Uncertainty in LLM-Based Systems
arXiv CS.LG
•
ArXi:2506.10060v2 Announce Type: replace Although large language models (LLMs) are becoming increasingly capable of solving challenging real-world tasks, accurately quantifying their uncertainty remains a critical open problem--one that limits their applicability in high-stakes domains. This challenge is further compounded by the closed-source, black-box nature of many state-of-the-art LLMs. Moreover, LLM-based systems can be highly sensitive to the prompts that bind them together, which often require significant manual tuning (i.e., prompt engineering