Exploring Coding Spot: Understanding Parametric Contributions to LLM Coding Performance
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arXiv
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| Auteurs principaux: | , , , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866909422144454656 |
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| author | Kim, Dongjun Kim, Minhyuk Chun, YongChan Park, Chanjun Lim, Heuiseok |
| author_facet | Kim, Dongjun Kim, Minhyuk Chun, YongChan Park, Chanjun Lim, Heuiseok |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_07113 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Exploring Coding Spot: Understanding Parametric Contributions to LLM Coding Performance Kim, Dongjun Kim, Minhyuk Chun, YongChan Park, Chanjun Lim, Heuiseok Computation and Language 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. |
| title | Exploring Coding Spot: Understanding Parametric Contributions to LLM Coding Performance |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2412.07113 |