Enterprise AI Analysis
Predicate Renaming via Large Language Models
This report details an innovative approach to leverage Large Language Models (LLMs) for automatically assigning meaningful names to predicates in logic programming, enhancing readability and interpretability.
Executive Impact Summary
Our analysis reveals significant potential for LLM-driven predicate renaming, streamlining logic programming development and improving system maintainability across various enterprise applications.
0%
Improvement in Readability
0h
Developer Hours Saved Annually
0%
Reduction in Debugging Time
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
Understanding the Renaming Pipeline
Our methodology outlines a three-step pipeline: LLMs suggest names, choose the most suitable one, and then LLM judges score and rank the suggestions. This process tackles the challenge of unnamed predicates in complex logic rules.
Predicate Renaming Workflow
Input Logic Rules
→
LLM Suggests Names
→
LLM Chooses Best Name
→
LLM Judges & Ranks
→
Output Renamed Rules
Key Findings & Performance
Experiments on various hand-crafted logic rules and a real-world dataset demonstrate the potential of LLMs for this task. Top models achieved high accuracy in assigning semantically meaningful predicate names.
7/7
Top Models Achieve Perfect Renaming in Coauthors Case Study
Family Relationship Rules Case Study
An ILP system generated rules for family relationships with unnamed predicates like h0, h1, h2, h3, h4. LLMs successfully assigned 'parent', 'grandparent', 'commonAncestor', 'siblings', and 'cousins' to these predicates, demonstrating strong contextual understanding.
ho(X,Y) :- mother(X,Y).
ho(X,Y) :- father(X,Y).
h1(X,Y) :- h0(X,Z), h0(Z,Y).| Predicate | Zero-Shot Accuracy | Few-Shot Accuracy |
|---|---|---|
| isNumber | 80% | 90% |
| isEven | 90% | 95% |
| isDivisor | 10% | 50% |
| gcd | 30% | 60% |
Calculate Your Potential ROI
See how LLM-driven predicate renaming can impact your development efficiency and cost savings.
Your AI Implementation Roadmap
A structured approach to integrate LLM-powered predicate renaming into your workflow.
Phase 1: Discovery & Assessment
Evaluate current logic programming practices, identify areas with unnamed predicates, and define specific renaming goals and success metrics.
Phase 2: LLM Integration & Customization
Integrate selected LLMs into your development environment, fine-tune models if necessary with domain-specific knowledge, and configure the renaming pipeline.
Phase 3: Pilot & Feedback
Conduct a pilot project on a subset of your logic rules, gather developer feedback, and iterate on prompt engineering and output standardization.
Phase 4: Full-Scale Deployment & Monitoring
Roll out the LLM-powered renaming across your entire codebase, establish continuous monitoring for quality, and provide ongoing training and support.
Ready to Enhance Your Logic Programs?
Automate predicate renaming and unlock new levels of clarity and maintainability for your enterprise AI initiatives.
Ready to Get Started?
Book Your Free Consultation.
Let's Discuss Your AI Strategy!
Lets Discuss Your Needs
Select a Date
Sun
Mon
Tue
Wed
Thu
Fri
Sat