Exploring Coding Spot: Understanding Parametric Contributions to LLM Coding Performance

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Authors: Dongjun Kim, Min-Hyuk Kim, Yongchan Chun, C. J. Park, Heuiseok Lim
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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.

Fields of Study & Subjects

Advanced Data Storage Technologies

Publication details

Journal / source
Findings of the Association for Computational Linguistics: ACL 2026
Publisher
Association for Computational Linguistics
Pages
28740–28746
Publication date
1 January 2026
Language
EN