How Programming Concepts and Neurons Are Shared in Code Language Models (2506.01074v1)

Abstract: Several studies have explored the mechanisms of 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.

Analysis of Programming Concepts and Neuron Sharing in Code LLMs

The paper "How Programming Concepts and Neurons Are Shared in Code LLMs" presents a detailed exploration of how LLMs trained on multiple programming languages internally represent these languages and identify structural patterns within their concept space. This paper is crucial for understanding how LLMs, such as those based on the Llama architecture, manage multilingual code processing tasks and share neuronal representations across different programming languages.

The research addresses two primary questions: whether an LLM uses English or a programming language (PL) as a pivot in code translation tasks and the extent to which language-specific neurons can be identified for different PLs within LLMs. To investigate these hypotheses, the authors employ a few-shot translation task across 21 programming language pairs using two Llama-based models: CodeLlama 7B and Llama 3.1 8B.

Key Findings

1. Concept Space Proximity to English: Analysis of the embeddings from intermediate layers shows that the concept space of LLMs in programming tasks is aligned more closely with English, particularly English tokens which include PL keywords. This alignment becomes increasingly evident in the subsequent layers of the models.
2. Neuron Activation Patterns: By studying neuron activations, the authors detect language-specific neurons primarily concentrated in the lower layers of the models. The various PLs show distinct activation patterns; however, languages with significant overlap and alignment, such as C# with others, exhibit fewer identifiable neuron distinctions due to shared influences.
3. Logit Lens Technique: The use of the logit lens method demonstrates that English and common PL tokens surface predominantly in intermediate model layers, with token probabilities peaking halfway through the network. This insight suggests an intrinsic preference for English-related embeddings across different tasks and input languages.
4. Cross-Lingual Alignment with MEXA: The paper also applies the MEXA alignment score to measure cross-lingual alignment and finds that certain languages, like C#, exhibit strong alignment with multiple PLs. This suggests a possibility for efficient language transfer and model adaptation without extensive retraining.
5. Language Activation Probability Entropy (LAPE): The LAPE methodology aids in identifying language-specific neurons, showing that although some neurons are dedicated to particular languages, especially in bottom layers, neurons at the top layers tend to include those exclusive to highly aligned languages such as C# and Java.

Implications

This research has both theoretical and practical implications. Theoretically, it extends the understanding of the internal representations of neural networks in processing code and their similarities to semantic processing in natural language, reinforcing the notion of shared neuron activations across languages. Practically, the paper provides crucial insights into building more efficient, versatile multilingual models that can cater to multilingual code generation and understanding tasks. By leveraging high cross-lingual alignment and leveraging shared neuron activations, developers could optimize models for better generalization and adaptation, thereby reducing the computational cost of model training and enhancing performance on specific language tasks.

Future Speculations

The trajectory of this research could pave the way for more robust, adaptable LLMs capable of handling a wider array of PLs through shared representation mechanisms. This could lead to more effective model architectures specializing in not just semantic code understanding, but also optimized training strategies for model pre-training in diverse linguistic environments. The findings also highlight the potential of modular neural networks where commonalities in language-specific neuron patterns could facilitate efficient incremental learning and adaptation to new programming languages as the tech landscape evolves.

By understanding and utilizing the shared embeddings and neuron activations in LLMs, this research opens the door to advanced applications and adaptation strategies in artificial intelligence, accelerating the development of tools and systems that can efficiently process and generate code across multiple languages.