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Guiding LLM Temporal Logic Generation with Explicit Separation of Data and Control

arXiv 2406.07400 ↗First posted 11 Jun 2024cs.LG cs.LOcited by 8

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Abstract (arXiv)

Temporal logics are powerful tools that are widely used for the synthesis and verification of reactive systems. The recent progress on Large Language Models (LLMs) has the potential to make the process of writing such specifications more accessible. However, writing specifications in temporal logics remains challenging for all but the most expert users. A key question in using LLMs for temporal logic specification engineering is to understand what kind of guidance is most helpful to the LLM and the users to easily produce specifications. Looking specifically at the problem of reactive program synthesis, we explore the impact of providing an LLM with guidance on the separation of control and data--making explicit for the LLM what functionality is relevant for the specification, and treating the remaining functionality as an implementation detail for a series of pre-defined functions and predicates. We present a benchmark set and find that this separation of concerns improves specification generation. Our benchmark provides a test set against which to verify future work in LLM generation of temporal logic specifications.

Key facts. Cited by 8 papers in the map (data through 7 Oct 2026). Builds on 5 of its 13 references that are in the map. First posted on arXiv 11 Jun 2024.

Builds on

5 of 13 references.

References · 5

  1. 2023SWE-bench: Can Language Models Resolve Real-World GitHub Issues?cited by 3,193
  2. 2023nl2spec: Interactively Translating Unstructured Natural Language to Temporal Logics with Large Language Modelscited by 57
  3. 2024Procedural Adherence and Interpretability Through Neuro-Symbolic Generative Agentscited by 5
  4. 2014Parameterized Synthesis Case Study: AMBA AHB (extended version)cited by 1
  5. 2021Syntroids: Synthesizing a Game for FPGAs using Temporal Logic Specificationscited by 1

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 1; 2025: 3; 2026: 4.

8 papers.