LLMs' Understanding of Compiler Code

LLMs' Understanding of Compiler Code

First empirical study on how well LLMs comprehend Intermediate Representations

This pioneering research evaluates large language models' ability to understand and work with Intermediate Representations (IRs) - the crucial connective layer in modern compilers.

  • Models like GPT-4 and LLaMA 3.1 were tested across four IR tasks: control flow reconstruction, decompilation, summarization, and execution reasoning
  • Performance varies significantly between models with GPT-4 demonstrating the strongest capabilities
  • Results highlight both possibilities and limitations for using LLMs in compiler toolchains and program analysis

This research opens new pathways for integrating AI into compiler design, code security analysis, and automated programming education tools - potentially transforming how we build and understand software systems.

Original Paper: Can Large Language Models Understand Intermediate Representations?

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