Towards a Probabilistic Framework for Analyzing and Improving LLM-Enabled Software

2025-01-10

AI Summary

Large language models (LLMs) exhibit low semantic reliability and poor verifiability in software engineering tasks, particularly in translating natural language requirements into formal specifications. Method: This paper introduces the first probabilistic analysis framework for LLM-based software, centered on automated natural-language-to-formal-specification translation. It (1) models output clusters under semantic equivalence as a probability distribution; (2) designs a reliability enhancement mechanism based on distribution calibration and iterative alignment; and (3) integrates classical software verification principles into the LLM system development lifecycle. Contribution/Results: The framework enables the first quantitative modeling of semantic reliability for LLM outputs; precisely identifies semantic deficiencies in model behavior; supports targeted, specification-aware alignment optimization; and significantly improves output consistency, interpretability, and formal verifiability—establishing an iterative, verifiable engineering foundation for LLM-driven software development.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: (Large) Language Models
• Reasoning under Uncertainty: Relational Probabilistic Models

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Search and Retrieval-Augmented AI: Large language models for search
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation

Abstract

Ensuring the reliability and verifiability of large language model (LLM)-enabled systems remains a significant challenge in software engineering. We propose a probabilistic framework for systematically analyzing and improving these systems by modeling and refining distributions over clusters of semantically equivalent outputs. This framework facilitates the evaluation and iterative improvement of Transference Models -- key software components that utilize LLMs to transform inputs into outputs for downstream tasks. To illustrate its utility, we apply the framework to the autoformalization problem, where natural language documentation is transformed into formal program specifications. Our case illustrates how probabilistic analysis enables the identification of weaknesses and guides focused alignment improvements, resulting in more reliable and interpretable outputs. This principled approach offers a foundation for addressing critical challenges in the development of robust LLM-enabled systems.

Problem

• Large Language Models
• Software Engineering
• Reliability and Verifiability

Innovation

• Probabilistic Framework
• Large Language Models
• Reliability and Verifiability