Scholar4AI Updated 2026-11-03 14:43 UTC+09:00

tatsu-lab/stanford_alpaca: papers linking to it

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

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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.

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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#

2024: 2 papers2025: 9 papers2026: 8 papers
20242026
Papers linking to tatsu-lab/stanford_alpaca by publication year
YearPapers
20242
20259
20268

Journals#

Journals whose papers link to tatsu-lab/stanford_alpaca most
JournalPapers
Expert Systems with Applications4
Neural Networks3
Knowledge-Based Systems2
Pattern Recognition2
Information Fusion2

Family tree#

Each paper sits under the earlier paper it builds on; papers that cite no other paper here start a branch of their own.

Papers linking to tatsu-lab/stanford_alpaca, most cited first
PublishedTitleJournalAuthorsCitationsLink
2024-05-27Vision-Enabled Large Language and Deep Learning Models for Image-Based Emotion RecognitionCognitive ComputationMohammad Nadeem, Shahab Saquib Sohail, Laeeba Javed et al.39cites it
2025-08-09LoRA dropout as a sparsity regularizer for overfitting reductionKnowledge-Based SystemsYang Lin, Xinyu Ma, Xu Chu et al.19cites it
2024-04-10ChatGPT for GTFS: benchmarking LLMs on GTFS semantics... and retrievalPublic TransportSaipraneeth Devunuri, Shirin Qiam, Lewis Lehe13cites it
2026-09-25Parameter-Efficient Fine-Tuning for Foundation ModelsInternational Journal of Computer VisionDan Zhang, Tao Feng, Lilong Xue et al.7cites it
2025-02-25Multi-objective math problem generation using large language model through an adaptive multi-level retrieval augmentation frameworkInformation FusionJianwen Sun, Wangzi Shi, Xiaoxuan Shen et al.6cites it
2025-06-20The future of open human feedbackNature Machine IntelligenceShachar Don-Yehiya, Ben Burtenshaw, Ramon Fernandez Astudillo et al.5cites it
2025-08-12Reasoning elicitation and multi-granularity contrastive learning for text-rich image understanding in large vision-language modelsPattern RecognitionJiazhi Xia, Xiangyu Zhu, Bingchuan Jiang et al.5cites it
2025-09-04Jailbreak attack with multimodal virtual scenario hypnosis for vision-language modelsPattern RecognitionXiayang Shi, Shangfeng Chen, Gang Zhang et al.4cites it
2025-08-22Pay more attention to the robustness of LLMs on adversarial prompt for instruction data miningNeural NetworksQiang Wang, Dawei Feng, Xu Zhang et al.1cites it
2025-10-05Scaling down, Powering up: A Survey on the Advancements of Small Vision-Language ModelsInformation FusionSheikh Iftekhar Ahmed, Muhammad Zubair Hasan, Abrar Jahin Niloy et al.1cites it
2026-04-25Optimizing speculative decoding via dynamic multi-path and dual-stream networksNeural NetworksYong Yang, Ting Yang, Shao Shuai Gao et al.1cites it
2026-05-14Model compression-driven instruction data mining framework with integrated adversarial attack strategiesExpert Systems with ApplicationsQiang Wang, Ao Shen, Yifan Zeng et al.1cites it
2025-10-19Addressing task conflicts in LLMs multi-task fine-tuning with task-specific subnetwork refinementMachine LearningYiqun Wang, Chaoqun Wan, Xiang Tian et al.0cites it
2025-12-11LaRA: Layer-wise rank allocation for efficient fine-tuning of pruned large language modelsInformation Processing & ManagementYuhua Zhou, Changhai Zhou, Shiyang Zhang et al.0cites it
2026-01-03GROVE : Hybrid data selection strategies for cost-effective instruction tuningExpert Systems with ApplicationsJeesu Jung, Jeesu Jung, Taewook Hwang et al.0cites it
2026-01-27Exploring and enhancing the transfer of distribution in knowledge distillation for autoregressive language modelsKnowledge-Based SystemsJun Rao, Xuebo Liu, Zepeng Lin et al.0cites it
2026-03-22Self-bootstrapping automated program repair: using LLMs to generate and evaluate synthetic training data for bug repairExpert Systems with ApplicationsDavid de-Fitero-Dominguez, Antonio García‐Cabot, Eva García‐López0cites it
2026-06-11Semantic-aware pruning of large language models via neuron importance explanationExpert Systems with ApplicationsYang Ji, Ying Sun, Jin Li et al.0cites it
2026-06-25ResVaRe: Parameter-efficient fine-tuning for large language models via cross-layer residual vector adaptation and representation editingNeural NetworksYue Wang, YingJie Li, Yue Wang0cites 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

@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}
}

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