Researchers find LLMs are bad at logical inference, good at “fluent nonsense”
arstechnica.com·10h·
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Show HN: Generative Graphics LLM Benchmark
ggbench.com·12h·
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Diffusion Language Models are Super Data Learners
jinjieni.notion.site·1d·
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Fundamentals of Probability
ocw.mit.edu·1d·
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Foundation models are going multimodal
twelvelabs.io·16h·
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5 Tips for Safe LLM Development - Infobip Developers Hub
infobip.com·16h·
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Einstein Fields: A Neural Perspective to Computational General Relativity
arxiviq.substack.com·2h·
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Recent cross-research on LLM and RL on ArXiv
github.com·1d·
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We Fixed AI's Broken Promise
understoryai.substack.com·15h·
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Database.news – curated list of database news from authoritative sources
database.news·11h·
Discuss: Lobsters, Hacker News
Programming languages and dimensions of units of measure
cl.cam.ac.uk·1d·
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Human Data Is (Probably) More Expensive Than Compute for Training Frontier LLMs
ddkang.substack.com·7h·
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Conversations remotely detected from cell phone vibrations, researchers report
psu.edu·1d·
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Scaling Interpretability
anthropic.com·19h·
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LLMs aren’t world models
yosefk.com·2d·
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Understanding reinforcement learning for model training from scratch
medium.com·1d·
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Forward Deployed AI Research
publish.obsidian.md·19h·
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Scaling Deep Learning Education in a College of Arts & Sciences
cacm.acm.org·4d·
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Different isn't always better, better is always different
cory.news·7h·
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Using Topological Data Analysis and Sparse Autoencoders to Evaluate TruthfulQA
bluelightai.com·1d·
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