---
title: "tatsu-lab/stanford_alpaca: papers citing it"
description: "19 papers in the tracked journals link to the GitHub repository tatsu-lab/stanford_alpaca in their Crossref records, the first in 2024; most of them (9) in 2025."
url: https://scholar4ai.com/code/github-tatsu-lab-stanford-alpaca
markdown_url: https://scholar4ai.com/code/github-tatsu-lab-stanford-alpaca.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
share_text: "19 papers in the tracked journals link to the GitHub repository tatsu-lab/stanford_alpaca in their Crossref records, the first in 2024; most of them (9) in 2025. (as of 2026-11-03)"
data_url: https://scholar4ai.com/data/rankings.json
updated: 2026-11-03T14:43:26+09:00
window: 2026-10-05 to 2026-11-03
source: OpenAlex (CC0)
---

# tatsu-lab/stanford_alpaca: papers linking to it

Research papers in the 480 tracked journals that link to tatsu-lab/stanford_alpaca (https://github.com/tatsu-lab/stanford_alpaca), read from their Crossref records: by year, category and journal, the family tree of papers building on one another, and the linked repositories derived from it or that it derives from.

- GitHub
- 30,232 stars
- 3,961 forks
- Python
- license Apache-2.0
- created 2023
- https://github.com/tatsu-lab/stanford_alpaca

All repositories: https://scholar4ai.com/code.md

## Key findings

- 19 papers in the tracked journals link to the GitHub repository tatsu-lab/stanford_alpaca in their Crossref records, the first in 2024; most of them (9) in 2025.
- By category: AI (18) and Operations Research (1); the journals with the most are Expert Systems with Applications (4), Neural Networks (3) and Knowledge-Based Systems (2).
- 2 of the papers build on one another: a later paper cites an earlier one that also links tatsu-lab/stanford_alpaca.

## By year

*Papers linking to tatsu-lab/stanford_alpaca by publication year*

| Year | Papers |
|---|---:|
| 2024 | 2 |
| 2025 | 9 |
| 2026 | 8 |

## Journals

*Journals whose papers link to tatsu-lab/stanford_alpaca most*

| Journal | Papers |
|---|---:|
| [Expert Systems with Applications](https://scholar4ai.com/journals/expert-systems-with-applications.md) | 4 |
| [Neural Networks](https://scholar4ai.com/journals/neural-networks.md) | 3 |
| [Knowledge-Based Systems](https://scholar4ai.com/journals/knowledge-based-systems.md) | 2 |
| [Pattern Recognition](https://scholar4ai.com/journals/pattern-recognition.md) | 2 |
| [Information Fusion](https://scholar4ai.com/journals/information-fusion.md) | 2 |

## Family tree

Each paper sits under the earlier paper it builds on.

- [Pay more attention to the robustness of LLMs on adversarial prompt for instruction data mining](https://doi.org/10.1016/j.neunet.2025.107989) (Neural Networks, 2025)
  - [Model compression-driven instruction data mining framework with integrated adversarial attack strategies](https://doi.org/10.1016/j.eswa.2026.132870) (Expert Systems with Applications, 2026) — cites the paper above

## Papers

*Papers linking to tatsu-lab/stanford_alpaca, most cited first*

| Published | Title | Journal | Authors | Citations | Link |
|---|---|---|---|---:|---|
| 2024-05-27 | [Vision-Enabled Large Language and Deep Learning Models for Image-Based Emotion Recognition](https://doi.org/10.1007/s12559-024-10281-5) | [Cognitive Computation](https://scholar4ai.com/journals/cognitive-computation.md) | Mohammad Nadeem, Shahab Saquib Sohail, Laeeba Javed et al. | 39 | cites it |
| 2025-08-09 | [LoRA dropout as a sparsity regularizer for overfitting reduction](https://doi.org/10.1016/j.knosys.2025.114241) | [Knowledge-Based Systems](https://scholar4ai.com/journals/knowledge-based-systems.md) | Yang Lin, Xinyu Ma, Xu Chu et al. | 19 | cites it |
| 2024-04-10 | [ChatGPT for GTFS: benchmarking LLMs on GTFS semantics... and retrieval](https://doi.org/10.1007/s12469-024-00354-x) | [Public Transport](https://scholar4ai.com/journals/public-transport.md) | Saipraneeth Devunuri, Shirin Qiam, Lewis Lehe | 13 | cites it |
| 2026-09-25 | [Parameter-Efficient Fine-Tuning for Foundation Models](https://doi.org/10.1007/s11263-026-03004-w) | [International Journal of Computer Vision](https://scholar4ai.com/journals/international-journal-of-computer-vision.md) | Dan Zhang, Tao Feng, Lilong Xue et al. | 7 | cites it |
| 2025-02-25 | [Multi-objective math problem generation using large language model through an adaptive multi-level retrieval augmentation framework](https://doi.org/10.1016/j.inffus.2025.103037) | [Information Fusion](https://scholar4ai.com/journals/information-fusion.md) | Jianwen Sun, Wangzi Shi, Xiaoxuan Shen et al. | 6 | cites it |
| 2025-06-20 | [The future of open human feedback](https://doi.org/10.1038/s42256-025-01038-2) | [Nature Machine Intelligence](https://scholar4ai.com/journals/nature-machine-intelligence.md) | Shachar Don-Yehiya, Ben Burtenshaw, Ramon Fernandez Astudillo et al. | 5 | cites it |
| 2025-08-12 | [Reasoning elicitation and multi-granularity contrastive learning for text-rich image understanding in large vision-language models](https://doi.org/10.1016/j.patcog.2025.112278) | [Pattern Recognition](https://scholar4ai.com/journals/pattern-recognition.md) | Jiazhi Xia, Xiangyu Zhu, Bingchuan Jiang et al. | 5 | cites it |
| 2025-09-04 | [Jailbreak attack with multimodal virtual scenario hypnosis for vision-language models](https://doi.org/10.1016/j.patcog.2025.112391) | [Pattern Recognition](https://scholar4ai.com/journals/pattern-recognition.md) | Xiayang Shi, Shangfeng Chen, Gang Zhang et al. | 4 | cites it |
| 2025-08-22 | [Pay more attention to the robustness of LLMs on adversarial prompt for instruction data mining](https://doi.org/10.1016/j.neunet.2025.107989) | [Neural Networks](https://scholar4ai.com/journals/neural-networks.md) | Qiang Wang, Dawei Feng, Xu Zhang et al. | 1 | cites it |
| 2025-10-05 | [Scaling down, Powering up: A Survey on the Advancements of Small Vision-Language Models](https://doi.org/10.1016/j.inffus.2025.103805) | [Information Fusion](https://scholar4ai.com/journals/information-fusion.md) | Sheikh Iftekhar Ahmed, Muhammad Zubair Hasan, Abrar Jahin Niloy et al. | 1 | cites it |
| 2026-04-25 | [Optimizing speculative decoding via dynamic multi-path and dual-stream networks](https://doi.org/10.1016/j.neunet.2026.109014) | [Neural Networks](https://scholar4ai.com/journals/neural-networks.md) | Yong Yang, Ting Yang, Shao Shuai Gao et al. | 1 | cites it |
| 2026-05-14 | [Model compression-driven instruction data mining framework with integrated adversarial attack strategies](https://doi.org/10.1016/j.eswa.2026.132870) | [Expert Systems with Applications](https://scholar4ai.com/journals/expert-systems-with-applications.md) | Qiang Wang, Ao Shen, Yifan Zeng et al. | 1 | cites it |
| 2025-10-19 | [Addressing task conflicts in LLMs multi-task fine-tuning with task-specific subnetwork refinement](https://doi.org/10.1007/s10994-025-06885-z) | [Machine Learning](https://scholar4ai.com/journals/machine-learning.md) | Yiqun Wang, Chaoqun Wan, Xiang Tian et al. | 0 | cites it |
| 2025-12-11 | [LaRA: Layer-wise rank allocation for efficient fine-tuning of pruned large language models](https://doi.org/10.1016/j.ipm.2025.104538) | [Information Processing & Management](https://scholar4ai.com/journals/information-processing-management.md) | Yuhua Zhou, Changhai Zhou, Shiyang Zhang et al. | 0 | cites it |
| 2026-01-03 | [GROVE : Hybrid data selection strategies for cost-effective instruction tuning](https://doi.org/10.1016/j.eswa.2025.131058) | [Expert Systems with Applications](https://scholar4ai.com/journals/expert-systems-with-applications.md) | Jeesu Jung, Jeesu Jung, Taewook Hwang et al. | 0 | cites it |
| 2026-01-27 | [Exploring and enhancing the transfer of distribution in knowledge distillation for autoregressive language models](https://doi.org/10.1016/j.knosys.2026.115382) | [Knowledge-Based Systems](https://scholar4ai.com/journals/knowledge-based-systems.md) | Jun Rao, Xuebo Liu, Zepeng Lin et al. | 0 | cites it |
| 2026-03-22 | [Self-bootstrapping automated program repair: using LLMs to generate and evaluate synthetic training data for bug repair](https://doi.org/10.1016/j.eswa.2026.132154) | [Expert Systems with Applications](https://scholar4ai.com/journals/expert-systems-with-applications.md) | David de-Fitero-Dominguez, Antonio García‐Cabot, Eva García‐López | 0 | cites it |
| 2026-06-11 | [Semantic-aware pruning of large language models via neuron importance explanation](https://doi.org/10.1016/j.eswa.2026.133192) | [Expert Systems with Applications](https://scholar4ai.com/journals/expert-systems-with-applications.md) | Yang Ji, Ying Sun, Jin Li et al. | 0 | cites it |
| 2026-06-25 | [ResVaRe: Parameter-efficient fine-tuning for large language models via cross-layer residual vector adaptation and representation editing](https://doi.org/10.1016/j.neunet.2026.109273) | [Neural Networks](https://scholar4ai.com/journals/neural-networks.md) | Yue Wang, YingJie Li, Yue Wang | 0 | cites it |

## How this is measured

Links are read from each paper's Crossref record as its publisher deposited it: the URLs and repository DOIs in its reference list (the software and data it cites) and its supplementary links (“is-supplemented-by”: its own code or data). A paper that gives its code only in the text, a footnote or a data availability statement is not counted, and some publishers deposit reference lists without their URLs, so every count here is a lower bound. Repository details come from the GitHub REST API (stars, forks, and the repository a fork was made from), the Hugging Face Hub API (likes, downloads, and the base model a model was derived from) and DataCite (Zenodo and other data DOIs, and the GitHub release a Zenodo record archives), read again every month.

## Cite this page

Scholar4AI. (2026). tatsu-lab/stanford_alpaca: papers linking to it. Journal and Conference Research Rankings. Retrieved November 3, 2026, from https://scholar4ai.com/code/github-tatsu-lab-stanford-alpaca

```bibtex
@misc{scholar4ai-code-github-tatsu-lab-stanford-alpaca,
  author = {{Scholar4AI}},
  title = {tatsu-lab/stanford_alpaca: papers linking to it},
  year = {2026},
  howpublished = {\url{https://scholar4ai.com/code/github-tatsu-lab-stanford-alpaca}},
  urldate = {2026-11-03},
  note = {Journal and Conference Research Rankings; data from OpenAlex (CC0); licensed CC BY-NC 4.0}
}
```

License: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/).