Gabi Stanovsky
@gabistanovsky.bsky.social
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Assistant professor at the Hebrew University.
pinned post!
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
about 1 year ago
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Gabi Stanovsky
Kyle Lo
13 days ago
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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Gabi Stanovsky
Itay Itzhak @ COLM 🍁
7 months ago
🚨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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Gabi Stanovsky
11 months ago
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
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Gabi Stanovsky
Adi Simhi
12 months ago
🚨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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Gabi Stanovsky
Sebastian Gehrmann
12 months ago
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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A vote to stop defining what's LLMs at the start of every paper
about 1 year ago
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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
about 1 year ago
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0
4
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