# Large Language Models are Few-Shot Summarizers: Multi-Intent Comment Generation via In-Context Learning

arXiv: https://arxiv.org/abs/2304.11384 · HTML: https://mlsanity.com/paper/2304.11384/

## Key facts

- Cited by 72 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Apr 2023.

## Built on by (most cited first, 30 of 72)

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- [Source Code Summarization in the Era of Large Language Models](https://mlsanity.com/paper/2407.07959/) (2024)
- [Exploring Parameter-Efficient Fine-Tuning Techniques for Code Generation with Large Language Models](https://mlsanity.com/paper/2308.10462/) (2023)
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- [KADEL: Knowledge-Aware Denoising Learning for Commit Message Generation](https://mlsanity.com/paper/2401.08376/) (2024)
- [RAMBO: Enhancing RAG-based Repository-Level Method Body Completion](https://mlsanity.com/paper/2409.15204/) (2024)
- [A Deep Dive Into Large Language Model Code Generation Mistakes: What and Why?](https://mlsanity.com/paper/2411.01414/) (2024)
- [Model Editing for LLMs4Code: How Far are We?](https://mlsanity.com/paper/2411.06638/) (2024)
- [Output Format Biases in the Evaluation of Large Language Models for Code Translation](https://mlsanity.com/paper/2403.17214/) (2024)
- [Natural Language Outlines for Code: Literate Programming in the LLM Era](https://mlsanity.com/paper/2408.04820/) (2024)
- [Exploring the Security Threats of Knowledge Base Poisoning in Retrieval-Augmented Code Generation](https://mlsanity.com/paper/2502.03233/) (2025)
- [ESALE: Enhancing Code-Summary Alignment Learning for Source Code Summarization](https://mlsanity.com/paper/2407.01646/) (2024)

## Abstract (arXiv)

Code comment generation aims at generating natural language descriptions for a code snippet to facilitate developers' program comprehension activities. Despite being studied for a long time, a bottleneck for existing approaches is that given a code snippet, they can only generate one comment while developers usually need to know information from diverse perspectives such as what is the functionality of this code snippet and how to use it. To tackle this limitation, this study empirically investigates the feasibility of utilizing large language models (LLMs) to generate comments that can fulfill developers' diverse intents. Our intuition is based on the facts that (1) the code and its pairwise comment are used during the pre-training process of LLMs to build the semantic connection between the natural language and programming language, and (2) comments in the real-world projects, which are collected for the pre-training, usually contain different developers' intents. We thus postulate that the LLMs can already understand the code from different perspectives after the pre-training. Indeed, experiments on two large-scale datasets demonstrate the rationale of our insights: by adopting the in-context learning paradigm and giving adequate prompts to the LLM (e.g., providing it with ten or more examples), the LLM can significantly outperform a state-of-the-art supervised learning approach on generating comments with multiple intents. Results also show that customized strategies for constructing the prompts and post-processing strategies for reranking the results can both boost the LLM's performances, which shed light on future research directions for using LLMs to achieve comment generation.