Logo
ScienceStack
Table of Contents
Fetching ...
Sign In

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

Juan Manuel Baldonado, Flavia Bonomo-Braberman, Víctor Adrián Braberman

TL;DR

The paper addresses reliability and verifiability challenges in LLM-enabled software by modeling distributions over meaning-classes produced by Transference Models (TMs) and estimating empirical distributions $p(\text{meaning-class})$ via clustering. It defines alignment, concentration, and improvement, and introduces almost-sure improvement across an input distribution $D$ to guide principled refinement. A key contribution is the Illustrative autoformalization case study on translating NL docstrings to Dafny pre-/post-conditions, demonstrating that concentrating probability mass on the correct meaning-class and reducing concentrated misalignment yields targeted improvements (e.g., reducing misaligned cases from 6 to 2). The framework also extends to agentic AI by treating LLM-driven agents as MDPs, facilitating safety analyses through meaning-class distributions and trajectory evaluation, thereby offering a practical foundation for robust, interpretable LLM-enabled software engineering.

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 distribution-aware 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.

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

TL;DR

The paper addresses reliability and verifiability challenges in LLM-enabled software by modeling distributions over meaning-classes produced by Transference Models (TMs) and estimating empirical distributions via clustering. It defines alignment, concentration, and improvement, and introduces almost-sure improvement across an input distribution to guide principled refinement. A key contribution is the Illustrative autoformalization case study on translating NL docstrings to Dafny pre-/post-conditions, demonstrating that concentrating probability mass on the correct meaning-class and reducing concentrated misalignment yields targeted improvements (e.g., reducing misaligned cases from 6 to 2). The framework also extends to agentic AI by treating LLM-driven agents as MDPs, facilitating safety analyses through meaning-class distributions and trajectory evaluation, thereby offering a practical foundation for robust, interpretable LLM-enabled software engineering.

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 distribution-aware 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.
Paper Structure (14 sections, 1 figure, 3 tables)

This paper contains 14 sections, 1 figure, 3 tables.

Table of Contents

  1. Introduction
  2. Preliminaries
  3. Large Language Models
  4. Autoformalization
  5. Transference Models
  6. Distributions over Semantic Domains
  7. Computing Empirical Distributions of TMs
  8. The Framework
  9. Assumptions, Hypothesis and Rationale
  10. Defining Improvement
  11. Illustrative results on Autoformalization
  12. Related Work
  13. Conclusions and Future Work
  14. The case of Agentic AI

Figures (1)

  • Figure 1: Architecture of the illustrated baseline transference model