Towards Understanding What State Space Models Learn About Code
Researchers have developed SSM-Interpret, a new framework for analyzing State Space Models (SSMs) used in code understanding. The study found that SSMs initially capture syntactic and semantic structures better than Transformers but can forget some relations during fine-tuning. Architectural modifications based on these findings improved SSM performance by up to 6 MRR on the NLCodeSearch task, demonstrating the framework's utility in guiding model design. AI
IMPACT Provides insights into SSM behavior for code tasks, potentially leading to more efficient and effective code-generation or analysis models.