Anonymous Data Upload for Submission [D]

2026-05-23 · Source: Machine Learning · Field: Technology & Digital — Artificial Intelligence & Machine Learning · Depth: Intermediate, quick

Summary

A user inquired about anonymously uploading models for replication in academic submissions, specifically for conferences like ACL/EMNLP. The primary concern was whether using HuggingFace, which offers download tracking on a paid plan, would violate anonymity policies due to the potential for tracking, even if the service isn't utilized. A suggested solution involves uploading the research paper to arXiv under the same title as the conference submission. Subsequently, the models and data can be uploaded to HuggingFace and linked to the arXiv paper, providing both reviewer access for replication and broader visibility for the work. This approach aims to circumvent direct tracking while facilitating necessary review processes.

Key takeaway

For AI Scientists or Machine Learning Engineers submitting to conferences like ACL/EMNLP, providing models for replication requires careful anonymity management. HuggingFace's download tracking, even if unused, could pose a risk. You should upload your paper to arXiv with the submission's title, then link your HuggingFace models to that arXiv entry. This ensures reviewer access and visibility while preserving anonymity.

Key insights

Anonymity for academic submissions can be maintained by linking HuggingFace models to an arXiv paper, avoiding direct reviewer tracking.

Principles

• Preserve reviewer anonymity.
• Ensure model replication access.
• Combine visibility with blind review.

Method

Upload the research paper to arXiv using the submission's title. Then, upload models/data to HuggingFace and link them directly to the arXiv paper for reviewer access.

In practice

• Publish paper on arXiv.
• Link HuggingFace models to arXiv.
• Verify conference anonymity rules.

Topics

• Academic Publishing
• Peer Review
• Anonymity
• HuggingFace
• arXiv
• Model Replication

Best for: AI Scientist, Machine Learning Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Machine Learning.