Improving Code Understanding in Large Language Models through Concept-Aware Consistency Learning

2025-05-17 · Source: cs.SE updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Software Development & Engineering · Depth: Expert, extended

Summary

A novel framework named ProCURE has been introduced to enhance Large Language Models' (LLMs) understanding of fundamental "Programming Concepts Predicates" (PCPs), such as data flow and control flow, which current models often grasp shallowly. ProCURE employs a two-stage approach: first, an automated counterfactual code augmentation pipeline generates high-quality, concept-aligned datasets by perturbing original code while preserving functionality. This pipeline, utilizing gpt-4o, achieved an average success rate of 97.51% across HumanEval, MBPP, and CodeContests benchmarks, requiring only 1.24 attempts per transformation. Second, a concept-aware instruction fine-tuning method, incorporating a novel concept-sensitive loss function, trains LLMs on these augmented datasets. Evaluations on Llama3.1-8B, CodeLlama-13B, and StarCoder-7B demonstrated that ProCURE improved the "Concept Consistency Score" (CCS) by an average of 18.77%, outperforming a standard fine-tuning baseline by 10.47%, while also boosting Pass@1 by 0.7% and Pass@5 by 4.7%.

Key takeaway

For Machine Learning Engineers developing code-generating LLMs, you should integrate concept-aware fine-tuning to move beyond surface-level code understanding. By leveraging automated counterfactual data generation and a concept-sensitive loss, you can significantly improve your models' grasp of programming concepts like data and control flow, leading to more robust and generalizable code generation performance. This approach enhances reliability for critical software engineering applications.

Key insights

Concept-aware counterfactual data and a sensitive loss function significantly improve LLMs' deep understanding of programming concepts.

Principles

• Deep code understanding requires explicit concept-level supervision.
• Counterfactual data generation enhances model robustness.
• Concept-sensitive loss guides models beyond surface-level patterns.

Method

ProCURE automates counterfactual code generation using LLM-based prompts with static analysis, CoT, and one-shot examples, then fine-tunes models with concept-aware data sampling and a concept-sensitive loss function.

In practice

• Generate concept-oriented code perturbations for training.
• Apply concept-sensitive loss to emphasize programming logic.
• Evaluate code understanding using Concept Consistency Score (CCS).

Topics

• Large Language Models
• Code Understanding
• Counterfactual Augmentation
• Concept-Aware Fine-tuning
• Programming Concepts
• Code Generation

Best for: Research Scientist, AI Engineer, AI Scientist, Machine Learning Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.SE updates on arXiv.org.