Evaluation Cards: An Interpretive Layer for AI Evaluation Reporting
Paper • 2606.09809 • Published • 4
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evijit
authored a
paper 9 days ago
lenadan
updated a
dataset 14 days ago
Post
1084
Weekend mini project! Since commentary on AI is inherently interdisciplinary, we connected the observations in the Pope's encyclical with decades of scholarship in Responsible AI and Ethics research and created an interactive space with these annotations!
Work with @IJ-Reynolds , @yjernite , and @meg
Lots to unpack. We started with 105 annotations. Please submit pull requests for more that we may have missed!
society-ethics/annotated-encyclical
Work with @IJ-Reynolds , @yjernite , and @meg
Lots to unpack. We started with 105 annotations. Please submit pull requests for more that we may have missed!
society-ethics/annotated-encyclical
Asaf-Yehudai
submitted a
paper to Daily Papers 25 days ago
Elron
updated a
Space about 1 month ago
Elron
published a
Space about 1 month ago
evijit
submitted a
paper to Daily Papers about 1 month ago
lenadan
published a
dataset about 1 month ago
eliyahabba
submitted a
paper to Daily Papers 2 months ago
Elron
authored 7
papers 4 months ago
AlephBERT:A Hebrew Large Pre-Trained Language Model to Start-off your Hebrew NLP Application With
Paper • 2104.04052 • Published
Efficient Benchmarking (of Language Models)
Paper • 2308.11696 • Published
Quality Controlled Paraphrase Generation
Paper • 2203.10940 • Published
Lexical Generalization Improves with Larger Models and Longer Training
Paper • 2210.12673 • Published
Benchmark Agreement Testing Done Right: A Guide for LLM Benchmark Evaluation
Paper • 2407.13696 • Published • 5
DOVE: A Large-Scale Multi-Dimensional Predictions Dataset Towards Meaningful LLM Evaluation
Paper • 2503.01622 • Published
General Agent Evaluation
Paper • 2602.22953 • Published • 12
borgr
submitted a
paper to Daily Papers 4 months ago
Post
2890
AI for Scientific Discovery Won't Work Without Fixing How We Collaborate.
My co-author @cgeorgiaw and I just published a paper challenging a core assumption: that the main barriers to AI in science are technical. They're not. They're social.
Key findings:
🚨 The "AI Scientist" myth delays progress: Waiting for AGI devalues human expertise and obscures science's real purpose: cultivating understanding, not just outputs.
📊 Wrong incentives: Datasets have 100x longer impact than models, yet data curation is undervalued.
⚠️ Broken collaboration: Domain scientists want understanding. ML researchers optimize performance. Without shared language, projects fail.
🔍 Fragmentation costs years: Harmonizing just 9 cancer files took 329 hours.
Why this matters: Upstream bottlenecks like efficient PDE solvers could accelerate discovery across multiple sciences. CASP mobilized a community around protein structure, enabling AlphaFold. We need this for dozens of challenges.
Thus, we're launching Hugging Science! A global community addressing these barriers through collaborative challenges, open toolkits, education, and community-owned infrastructure. Please find all the links below!
Paper: AI for Scientific Discovery is a Social Problem (2509.06580)
Join:
hugging-science
Discord: https://discord.com/invite/VYkdEVjJ5J
My co-author @cgeorgiaw and I just published a paper challenging a core assumption: that the main barriers to AI in science are technical. They're not. They're social.
Key findings:
🚨 The "AI Scientist" myth delays progress: Waiting for AGI devalues human expertise and obscures science's real purpose: cultivating understanding, not just outputs.
📊 Wrong incentives: Datasets have 100x longer impact than models, yet data curation is undervalued.
⚠️ Broken collaboration: Domain scientists want understanding. ML researchers optimize performance. Without shared language, projects fail.
🔍 Fragmentation costs years: Harmonizing just 9 cancer files took 329 hours.
Why this matters: Upstream bottlenecks like efficient PDE solvers could accelerate discovery across multiple sciences. CASP mobilized a community around protein structure, enabling AlphaFold. We need this for dozens of challenges.
Thus, we're launching Hugging Science! A global community addressing these barriers through collaborative challenges, open toolkits, education, and community-owned infrastructure. Please find all the links below!
Paper: AI for Scientific Discovery is a Social Problem (2509.06580)
Join:
Discord: https://discord.com/invite/VYkdEVjJ5J
burtenshaw
authored a
paper 9 months ago
burtenshaw
posted an update 9 months ago
Post
8678
Smol course has a distinctive approach to teaching post-training, so I'm posting about how it’s different to other post-training courses, including the llm course that’s already available.
In short, the smol course is just more direct that any of the other course, and intended for semi-pro post trainers.
- It’s a minimal set of instructions on the core parts.
- It’s intended to bootstrap real projects you're working on.
- The material handsover to existing documentation for details
- Likewise, it handsover to the LLM course for basics.
- Assessment is based on a leaderboard, without reading all the material.
To start the smol course, follow here:
smol-course
In short, the smol course is just more direct that any of the other course, and intended for semi-pro post trainers.
- It’s a minimal set of instructions on the core parts.
- It’s intended to bootstrap real projects you're working on.
- The material handsover to existing documentation for details
- Likewise, it handsover to the LLM course for basics.
- Assessment is based on a leaderboard, without reading all the material.
To start the smol course, follow here: