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Shayne Longpre

Shayne

https://www.shaynelongpre.com

AI & ML interests

ML, NLP, Multilinguality, responsible/fair use of intelligent algorithms

Recent Activity

upvoted a paper 10 days ago

Consent in Crisis: The Rapid Decline of the AI Data Commons

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Organizations

Shayne's activity

upvoted a paper 10 days ago

Consent in Crisis: The Rapid Decline of the AI Data Commons

Paper • 2407.14933 • Published Jul 20 • 12

liked a dataset 2 months ago

PNLPhub/FarsInstruct

Viewer • Updated Sep 26 • 22.7M • 1.22k • 6

authored 4 papers 5 months ago

The Responsible Foundation Model Development Cheatsheet: A Review of Tools & Resources

Paper • 2406.16746 • Published Jun 24

Entity-Based Knowledge Conflicts in Question Answering

Paper • 2109.05052 • Published Sep 10, 2021

The Flan Collection: Designing Data and Methods for Effective Instruction Tuning

Paper • 2301.13688 • Published Jan 31, 2023 • 8

MKQA: A Linguistically Diverse Benchmark for Multilingual Open Domain Question Answering

Paper • 2007.15207 • Published Jul 30, 2020

authored a paper 7 months ago

Prometheus 2: An Open Source Language Model Specialized in Evaluating Other Language Models

Paper • 2405.01535 • Published May 2 • 118

authored a paper 9 months ago

On the Societal Impact of Open Foundation Models

Paper • 2403.07918 • Published Feb 27 • 16

Reacted to yjernite's post with 🤗❤️ 9 months ago

Post

👷🏽‍♀️📚🔨 Announcing the Foundation Model Development Cheatsheet!

My first 🤗Post🤗 ever to announce the release of a fantastic collaborative resource to support model developers across the full development stack: The FM Development Cheatsheet available here: https://fmcheatsheet.org/

The cheatsheet is a growing database of the many crucial resources coming from open research and development efforts to support the responsible development of models. This new resource highlights essential yet often underutilized tools in order to make it as easy as possible for developers to adopt best practices, covering among other aspects:
🧑🏼‍🤝‍🧑🏼 data selection, curation, and governance;
📖 accurate and limitations-aware documentation;
⚡ energy efficiency throughout the training phase;
📊 thorough capability assessments and risk evaluations;
🌏 environmentally and socially conscious deployment strategies.

We strongly encourage developers working on creating and improving models to make full use of the tools listed here, and to help keep the resource up to date by adding the resources that you yourself have developed or found useful in your own practice 🤗

Congrats to all the participants in this effort for the release! Read more about it from:
@Shayne - https://twitter.com/ShayneRedford/status/1763215814860186005
@hails and @stellaathena - https://blog.eleuther.ai/fm-dev-cheatsheet/
@alon-albalak - http://nlp.cs.ucsb.edu/blog/a-new-guide-for-the-responsible-development-of-foundation-models.html

And also to @gabrielilharco @sayashk @kklyman @kylel @mbrauh @fauxneticien @avi-skowron @Bertievidgen Laura Weidinger, Arvind Narayanan, @VictorSanh @Davlan @percyliang Rishi Bommasani, @breakend @sasha 🔥

1 reply

Reacted to VictorSanh's post with 👍 9 months ago

Post

An increasing number of engineers and researchers are developing foundational models. Navigating the tools, resources, codebases, and best practices guides is daunting for new contributors.

Introducing the Foundation Model Development Cheatsheet, a succinct guide with 250+ resources & tools for:
📖 sourcing data
🔍 documenting & audits
🌍 environmental impact
🥊 risks & harms eval
🎮 release & monitoring

https://fmcheatsheet.org/

👐 What tools & resources should appear in that cheatsheet? Contributions encouraged!

This is the result of a large collaboration between many organizations promoting open-science, and spearheaded by @Shayne 🔥