Exploring Large Language Models for Code Explanation
Paheli Bhattacharya, Manojit Chakraborty, Kartheek N S N Palepu, Pandey, Vikas, Ishan Dindorkar, Rajpurohit, Rakesh, Rishabh Gupta · arXiv (Cornell University) · 2023
Automating code documentation through explanatory text can prove highly beneficial in code understanding. Large Language Models (LLMs) have made remarkable strides in Natural Language Processing, especially within software engineering tasks such as code generation and code summarization. This study specifically delves into the task of generating natural-language summaries for code snippets, using various LLMs. The findings indicate that Code LLMs outperform their generic counterparts, and zero-shot methods yield superior results when dealing with datasets with dissimilar distributions between training and testing sets.