source: arXiv Machine Learning: PPDL: LLM-Based Flows as Probabilistic Programs
level: research
Researchers introduced a probabilistic programming language for building flows that chain multiple large language model calls and tools. The language allows developers to express application logic while automatically tracking and propagating uncertainty through each step. It also supports experimenting with inference scaling methods without modifying the flow’s core logic. The work aims to make LLM-based applications more reliable by giving users and developers clear confidence measures for outputs.
The paper includes an experimental study demonstrating the language’s ability to quantify uncertainty across different flow configurations. A case study applies it to build a theorem-proving agent for the Rocq theorem prover. The approach does not require additional code beyond the flow definition, reducing development overhead. However, the abstract does not provide specific performance metrics or compare uncertainty estimates against ground-truth confidence, leaving open questions about practical accuracy gains.
LLM outputs often lack calibrated confidence scores, making it hard to trust results in automated workflows. Existing frameworks for LLM chaining typically treat each call as deterministic, ignoring cumulative uncertainty. By treating flows as probabilistic programs, this work connects LLM orchestration with well-established techniques from probabilistic programming. For AI practitioners, it offers a path to more transparent and auditable systems, especially in high-stakes domains like formal verification or automated reasoning where reliability is critical.
why it matters: It gives developers a built-in way to measure and manage uncertainty in multi-step LLM applications, improving trust and reliability without extra coding effort.
source: arXiv Machine Learning: PPDL: LLM-Based Flows as Probabilistic Programs