BiGSCoder: State Space Model for Code Understanding

We present BiGSCoder, a novel encoder-only bidirectional state-space model (SSM) featuring a gated architecture, pre-trained for code understanding on a code dataset using masked language modeling. Our work aims to systematically evaluate SSMs' capabilities in coding tasks compared to traditional transformer architectures; BiGSCoder is built for this purpose. Through comprehensive experiments across diverse pre-training configurations and code understanding benchmarks, we demonstrate that BiGSCoder outperforms transformer-based models, despite utilizing simpler pre-training strategies and much less training data. Our results indicate that BiGSCoder can serve as a more sample-efficient alternative to conventional transformer models. Furthermore, our study shows that SSMs perform better without positional embeddings and can effectively extrapolate to longer sequences during fine-tuning.

A Critical Study of What Code-LLMs Learn

Large Language Models trained on code corpora (code-LLMs) have demonstrated impressive performance in various coding assistance tasks. However, despite their increased size and training dataset, code-LLMs still have limitations such as suggesting codes with syntactic errors, variable misuse etc. Some studies argue that code-LLMs perform well on coding tasks because they use self-attention and hidden representations to encode relations among input tokens. However, previous works have not studied what code properties are not encoded by code-LLMs. In this paper, we conduct a fine-grained analysis of attention maps and hidden representations of code-LLMs. Our study indicates that code-LLMs only encode relations among specific subsets of input tokens. Specifically, by categorizing input tokens into syntactic tokens and identifiers, we found that models encode relations among syntactic tokens and among identifiers, but they fail to encode relations between syntactic tokens and identifiers. We also found that fine-tuned models encode these relations poorly compared to their pre-trained counterparts. Additionally, larger models with billions of parameters encode significantly less information about code than models with only a few hundred million parameters.