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Gabi Stanovsky

@gabistanovsky.bsky.social

313 followers 226 following 4 posts

Assistant professor at the Hebrew University.

Posts Replies Media

Reposted by Gabi Stanovsky

Kyle Lo @ ICML2026 🇰🇷 @kylelo.bsky.social · 27/01/2026

The 5th Generation, Evaluation, and Metrics (GEM) Workshop will be at #ACL2026! Call for papers is out. Topics include: 🐟 LMs as evaluators 🐠 Living benchmarks 🍣 Eval with humans and more New for 2026: Opinion & Statement Papers! Full CFP: gem-workshop.com/call-for-pap...

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Reposted by Gabi Stanovsky

Itay Itzhak @ COLM 🍁 @itay-itzhak.bsky.social · 15/07/2025

🚨New paper alert🚨 🧠 Instruction-tuned LLMs show amplified cognitive biases — but are these new behaviors, or pretraining ghosts resurfacing? Excited to share our new paper, accepted to CoLM 2025🎉! See thread below 👇 #BiasInAI #LLMs #MachineLearning #NLProc

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Reposted by Gabi Stanovsky

shaharl6000.bsky.social @shaharl6000.bsky.social · 11/03/2025

Can RAG performance get * worse * with more relevant documents?📄 We put the number of retrieved documents in RAG to the test! 💥Preprint💥: arxiv.org/abs/2503.04388 1/3

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Adi Simhi @adisimhi.bsky.social · 19/02/2025

🚨New arXiv preprint!🚨 LLMs can hallucinate - but did you know they can do so with high certainty even when they know the correct answer? 🤯 We find those hallucinations in our latest work with @itay-itzhak.bsky.social, @fbarez.bsky.social, @gabistanovsky.bsky.social and Yonatan Belinkov

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Reposted by Gabi Stanovsky

Sebastian Gehrmann @sebgehr.bsky.social · 12/02/2025

GEM is so back! Our workshop for Generation, Evaluation, and Metrics is coming to an ACL near you. Evaluation in the world of GenAI is more important than ever, so please consider submitting your amazing work. CfP can be found at gem-benchmark.com/workshop

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Gabi Stanovsky @gabistanovsky.bsky.social · 06/02/2025

A vote to stop defining what's LLMs at the start of every paper

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Gabi Stanovsky @gabistanovsky.bsky.social · 03/02/2025

There's a lot of talk about regulating AI, but do regulators know the technology well enough? In our new paper, we survey major reg efforts & find they rely on benchmarking, which we know to be problematic. How did this happen & what can we do about it? arxiv.org/pdf/2501.15693

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