How Programming Concepts and Neurons Are Shared in Code Language Models
Several studies have explored the mechanisms of large language models (LLMs) in coding tasks, but most have focused on programming languages (PLs) in a monolingual setting. In this paper, we investigate the relationship between multiple PLs and English in the concept space of LLMs. We perform a few-shot translation task on 21 PL pairs using two Llama-based models. By decoding the embeddings of intermediate layers during this task, we observe that the concept space is closer to English (including PL keywords) and assigns high probabilities to English tokens in the second half of the intermediate layers. We analyze neuron activations for 11 PLs and English, finding that while language-specific neurons are primarily concentrated in the bottom layers, those exclusive to each PL tend to appear in the top layers. For PLs that are highly aligned with multiple other PLs, identifying language-specific neurons is not feasible. These PLs also tend to have a larger keyword set than other PLs and are closer to the model's concept space regardless of the input/output PL in the translation task. Our findings provide insights into how LLMs internally represent PLs, revealing structural patterns in the model's concept space. Code is available at https://github.com/cisnlp/code-specific-neurons.
683e923450c43488f7ad5e02
Amir Hossein Kargaran
claimed_verified
2025-06-03T08:39:12.648Z
61bf84c8ca59d6d196a1b4e8
https://cdn-avatars.huggingface.co/v1/production/uploads/61bf84c8ca59d6d196a1b4e8/5CTo8gpBXKeKA5cbQLU7b.jpeg
Amir Hossein Kargaran
kargaranamir
user
kargaranamir
683e923450c43488f7ad5e03
Yihong Liu
claimed_verified
2025-06-05T07:28:34.979Z
653f7e569e84d1e8b6a66e70
/avatars/24eaa6434508a162c349aebfc51990ff.svg
Yihong Liu
yihongLiu
user
yihongLiu
683e923450c43488f7ad5e04
François Yvon
683e923450c43488f7ad5e05
Hinrich Schütze
How Programming Concepts and Neurons Are Shared in Code Language Models
Several studies have explored the mechanisms of large language models (LLMs) in coding tasks, but most have focused on programming languages (PLs) in a monolingual setting. In this paper, we investigate the relationship between multiple PLs and English in the concept space of LLMs. We perform a few-shot translation task on 21 PL pairs using two Llama-based models. By decoding the embeddings of intermediate layers during this task, we observe that the concept space is closer to English (including PL keywords) and assigns high probabilities to English tokens in the second half of the intermediate layers. We analyze neuron activations for 11 PLs and English, finding that while language-specific neurons are primarily concentrated in the bottom layers, those exclusive to each PL tend to appear in the top layers. For PLs that are highly aligned with multiple other PLs, identifying language-specific neurons is not feasible. These PLs also tend to have a larger keyword set than other PLs and are closer to the model's concept space regardless of the input/output PL in the translation task. Our findings provide insights into how LLMs internally represent PLs, revealing structural patterns in the model's concept space. Code is available at https://github.com/cisnlp/code-specific-neurons.
Large language models encode programming languages in a concept space that shows stronger alignment with English, with language-specific neurons distributed across different model layers depending on language relationships.
large language models programming languages concept space few-shot translation Llama-based models embeddings intermediate layers neuron activations language-specific neurons