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Exploring Coding Spot: Understanding Parametric Contributions to LLM Coding Performance

Dongjun Kim, Minhyuk Kim, YongChan Chun, Chanjun Park, Heuiseok Lim

TL;DR

The paper investigates whether coding knowledge in LLMs is localized to dedicated parametric regions by introducing Coding Spot, a brain-inspired coding-specific parameter subset. It develops a gradient-based, cross-language framework to identify this region through language-specific importance scoring, cross-language aggregation, and an empirically determined top percentile selection. By systematically deactivating Coding Spot parameters in three LLMs, the study demonstrates that coding performance can collapse with tiny perturbations and that some general tasks are also affected, revealing a partially shared, polysemantic parametric organization. The findings have implications for understanding model interpretability, enabling targeted parameter-efficient tuning, and guiding architecture design to preserve coding robustness while maintaining broad cognitive capabilities.

Abstract

Large Language Models (LLMs) have demonstrated notable proficiency in both code generation and comprehension across multiple programming languages. However, the mechanisms underlying this proficiency remain underexplored, particularly with respect to whether distinct programming languages are processed independently or within a shared parametric region. Drawing an analogy to the specialized regions of the brain responsible for distinct cognitive functions, we introduce the concept of Coding Spot, a specialized parametric region within LLMs that facilitates coding capabilities. Our findings identify this Coding Spot and show that targeted modifications to this subset significantly affect performance on coding tasks, while largely preserving non-coding functionalities. This compartmentalization mirrors the functional specialization observed in cognitive neuroscience, where specific brain regions are dedicated to distinct tasks, suggesting that LLMs may similarly employ specialized parameter regions for different knowledge domains.

Exploring Coding Spot: Understanding Parametric Contributions to LLM Coding Performance

TL;DR

The paper investigates whether coding knowledge in LLMs is localized to dedicated parametric regions by introducing Coding Spot, a brain-inspired coding-specific parameter subset. It develops a gradient-based, cross-language framework to identify this region through language-specific importance scoring, cross-language aggregation, and an empirically determined top percentile selection. By systematically deactivating Coding Spot parameters in three LLMs, the study demonstrates that coding performance can collapse with tiny perturbations and that some general tasks are also affected, revealing a partially shared, polysemantic parametric organization. The findings have implications for understanding model interpretability, enabling targeted parameter-efficient tuning, and guiding architecture design to preserve coding robustness while maintaining broad cognitive capabilities.

Abstract

Large Language Models (LLMs) have demonstrated notable proficiency in both code generation and comprehension across multiple programming languages. However, the mechanisms underlying this proficiency remain underexplored, particularly with respect to whether distinct programming languages are processed independently or within a shared parametric region. Drawing an analogy to the specialized regions of the brain responsible for distinct cognitive functions, we introduce the concept of Coding Spot, a specialized parametric region within LLMs that facilitates coding capabilities. Our findings identify this Coding Spot and show that targeted modifications to this subset significantly affect performance on coding tasks, while largely preserving non-coding functionalities. This compartmentalization mirrors the functional specialization observed in cognitive neuroscience, where specific brain regions are dedicated to distinct tasks, suggesting that LLMs may similarly employ specialized parameter regions for different knowledge domains.

Paper Structure

This paper contains 17 sections, 4 equations, 1 figure, 2 tables.

Table of Contents

  1. Introduction
  2. Coding Spot
  3. Methodological Framework
  4. Parameter Importance Scoring
  5. Aggregating Parameter Importance Across Languages
  6. Defining the Coding Spot
  7. Experiments
  8. Experimental Setup
  9. Datasets
  10. Models
  11. Evaluation Metrics
  12. Results
  13. Main Results
  14. Impact of Coding Spot on Performance
  15. Conclusion
  16. ...and 2 more sections

Figures (1)

  • Figure 1: Overview of the framework for extracting and analyzing Coding Spot within LLMs. The process begins with the model undergoing importance scoring independently for n programming languages. The Importance Scores extracted from each language are aggregated for each parameter and then sorted in descending order. The parameters within the top k% of Importance Scores are defined as the Coding Spot.