# Selective Shot Learning for Code Explanation

arXiv: https://arxiv.org/abs/2412.12852 · HTML: https://mlsanity.com/paper/2412.12852/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 25 references that are in the map.
- First posted on arXiv 17 Dec 2024.

## Builds on (most cited first, 15 of 15)

- [An Empirical Evaluation of Using Large Language Models for Automated Unit Test Generation](https://mlsanity.com/paper/2302.06527/) (2023)
- [Learning to Mine Aligned Code and Natural Language Pairs from Stack Overflow](https://mlsanity.com/paper/1805.08949/) (2018)
- [A Transformer-based Approach for Source Code Summarization](https://mlsanity.com/paper/2005.00653/) (2020)
- [Few-shot training LLMs for project-specific code-summarization](https://mlsanity.com/paper/2207.04237/) (2022)
- [Natural Language to Code Generation in Interactive Data Science Notebooks](https://mlsanity.com/paper/2212.09248/) (2022)
- [Experiences from Using Code Explanations Generated by Large Language Models in a Web Software Development E-Book](https://mlsanity.com/paper/2211.02265/) (2022)
- [Automatic Semantic Augmentation of Language Model Prompts (for Code Summarization)](https://mlsanity.com/paper/2304.06815/) (2023)
- [Large Language Models are Few-Shot Summarizers: Multi-Intent Comment Generation via In-Context Learning](https://mlsanity.com/paper/2304.11384/) (2023)
- [A Comparative Analysis of Large Language Models for Code Documentation Generation](https://mlsanity.com/paper/2312.10349/) (2023)
- [Python Code Generation by Asking Clarification Questions](https://mlsanity.com/paper/2212.09885/) (2022)
- [Leveraging Code Generation to Improve Code Retrieval and Summarization via Dual Learning](https://mlsanity.com/paper/2002.10198/) (2020)
- [Developer-Intent Driven Code Comment Generation](https://mlsanity.com/paper/2302.07055/) (2023)
- [TAG : Type Auxiliary Guiding for Code Comment Generation](https://mlsanity.com/paper/2005.02835/) (2020)
- [Exploring Large Language Models for Code Explanation](https://mlsanity.com/paper/2310.16673/) (2023)
- [GenCodeSearchNet: A Benchmark Test Suite for Evaluating Generalization in Programming Language Understanding](https://mlsanity.com/paper/2311.09707/) (2023)

## Built on by (most cited first, 1 of 1)

- [An Evaluation of Context Length Extrapolation in Long Code via Positional Embeddings and Efficient Attention](https://mlsanity.com/paper/2602.21800/) (2026)

## Abstract (arXiv)

Code explanation plays a crucial role in the software engineering domain, aiding developers in grasping code functionality efficiently. Recent work shows that the performance of LLMs for code explanation improves in a few-shot setting, especially when the few-shot examples are selected intelligently. State-of-the-art approaches for such Selective Shot Learning (SSL) include token-based and embedding-based methods. However, these SSL approaches have been evaluated on proprietary LLMs, without much exploration on open-source Code-LLMs. Additionally, these methods lack consideration for programming language syntax. To bridge these gaps, we present a comparative study and propose a novel SSL method (SSL_ner) that utilizes entity information for few-shot example selection. We present several insights and show the effectiveness of SSL_ner approach over state-of-the-art methods across two datasets. To the best of our knowledge, this is the first systematic benchmarking of open-source Code-LLMs while assessing the performances of the various few-shot examples selection approaches for the code explanation task.