  Recently, code language models have achieved notable advancements in
addressing a diverse array of essential code comprehension and generation
tasks. Yet, the field lacks a comprehensive deep dive and understanding of the
code embeddings of multilingual code models. In this paper, we present a
comprehensive study on multilingual code embeddings, focusing on the
cross-lingual capabilities of these embeddings across different programming
languages. Through probing experiments, we demonstrate that code embeddings
comprise two distinct components: one deeply tied to the nuances and syntax of
a specific language, and the other remaining agnostic to these details,
primarily focusing on semantics. Further, we show that when we isolate and
eliminate this language-specific component, we witness significant improvements
in downstream code retrieval tasks, leading to an absolute increase of up to
+17 in the Mean Reciprocal Rank (MRR).

Computation and Language;Machine Learning