Towards More Trustworthy and Interpretable LLMs for Code through Syntax-Grounded Explanations

arXiv:2407.08983 · cs.SE, cs.AI, cs.LG · Submitted 2024-07-12 · Read on arXiv

cs.SE, cs.AI, cs.LG

Submitted: 2024-07-12

Updated: 2026-09-26

Code: https://github.com/numpy/numpy

Project page: https://christophm.github.io/interpretable-ml-book

Terminology

• Interpretability — 68× in this paper · explained in 15 episodes
• Accuracy — 17× in this paper · explained in 1 episode
• Alignment — 14× in this paper · explained in 1 episode
• Clustering — 14× in this paper · explained in 2 episodes
• Software Engineering — 13× in this paper
• Abstract Syntax Tree — 7× in this paper
• Causal Inference — 7× in this paper · explained in 4 episodes
• Explainability — 7× in this paper · explained in 3 episodes
• Programming Languages — 7× in this paper
• Robustness — 5× in this paper · explained in 5 episodes
• Syntax Category — 5× in this paper
• Token-Level Predictions — 5× in this paper
• Transformer — 5× in this paper · explained in 2 episodes
• Benchmark — 4× in this paper · explained in 2 episodes
• Aggregation — 3× in this paper · explained in 1 episode
• Calibration — 3× in this paper · explained in 7 episodes
• Fine-Tuning — 3× in this paper · explained in 5 episodes
• Grounding — 3× in this paper · explained in 3 episodes
• Structural Causal Model — 3× in this paper
• Adam — 2× in this paper · explained in 2 episodes
• Average Treatment Effect — 2× in this paper
• Downstream Tasks — 2× in this paper · explained in 2 episodes
• Natural Language Processing — 2× in this paper
• AI Alignment — 1× in this paper · explained in 2 episodes
• Accountability — 1× in this paper · explained in 2 episodes
• Attention Mechanism — 1× in this paper · explained in 2 episodes
• Benchmarking — 1× in this paper · explained in 3 episodes
• Byte-Pair Encoding — 1× in this paper
• Core — 1× in this paper · explained in 2 episodes
• Distillation — 1× in this paper · explained in 2 episodes
• Fidelity — 1× in this paper · explained in 2 episodes
• Generalization — 1× in this paper · explained in 13 episodes
• Large Language Models (LLMs) — 1× in this paper · explained in 6 episodes
• Shapley values — 1× in this paper · explained in 2 episodes
• Structural Causal Model (SCM) — 1× in this paper · explained in 4 episodes
• Tokenization — 1× in this paper · explained in 2 episodes

Sources

• Unified Pre-training for Program Understanding and Generation
• Sampling in Software Engineering Research: A Critical Review and Guidelines
• Evaluating Large Language Models Trained on Code
• An Empirical Study on the Usage of BERT Models for Code Completion
• Towards A Rigorous Science of Interpretable Machine Learning
• Comparing Explanation Methods for Traditional Machine Learning Models Part 1: An Overview of Current Methods and Quantifying Their Disagreement
• Towards Automatic Concept-based Explanations
• CodeSearchNet Challenge: Evaluating the State of Semantic Code Search
• Visualizing and Understanding Recurrent Networks
• On the Reliability and Explainability of Language Models for Program Generation
• Trustworthy and Synergistic Artificial Intelligence for Software Engineering: Vision and Roadmaps
• AST-Probe: Recovering abstract syntax trees from hidden representations of pre-trained language models
• Interpretable Machine Learning -- A Brief History, State-of-the-Art and Challenges
• Beyond Word Importance: Contextual Decomposition to Extract Interactions from LSTMs
• CodeBLEU: a Method for Automatic Evaluation of Code Synthesis
• Probing Pretrained Models of Source Code
• What Do They Capture? -- A Structural Analysis of Pre-Trained Language Models for Source Code
• A Systematic Literature Review on the Use of Deep Learning in Software Engineering Research
• A Systematic Evaluation of Large Language Models of Code

Related papers

• Falsification-Based Verification of LLM-Generated Optimization Models: Sound Test Batteries and Their Detection Limits
• GitSkills: A Dataset of Agent Skills on GitHub
• SABER: Benchmarking Operational Safety of LLM Coding Agents in Stateful Project Workspaces
• PackMonitor: Enabling Zero Package Hallucinations Through Decoding-Time Monitoring
• IntentCoding: Amplifying User Intent in Code Generation
• Incentives and Outcomes in Bug Bounties