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Nir Ratner

Hybrid Transformer–Mamba language models (Jamba)

Researcher behind AI21's long-context prompting, factuality evaluation, and hybrid-model work

A useful page for understanding the AI21 thread that connects long-context prompting tricks, factuality benchmarks, and modular language-model systems rather than treating those as separate subfields.

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AI21 Labs

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Known For

The ideas, systems, and research directions that make this person worth knowing.

01

Parallel Context Windows for long-context prompting

02

Factuality evaluation for language models

03

MRKL and Jamba-era AI21 language-model systems

04

Hybrid Transformer–Mamba language models (Jamba)

05

Jamba: A Hybrid Transformer-Mamba Language Model

06

Jamba-1.5: Hybrid Transformer-Mamba Models at Scale

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Canonical papers, project pages, or repositories that anchor this profile.

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Alan Arazi

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A valuable page in this cluster because his public role description is unusually specific: post-training, steerability, and AI-generated evaluation data are exactly the kinds of practical problems strong researcher pages should make discoverable.

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Clara Fridman

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A distinctive page in this AI21 cluster because she brings a linguistics and human-evaluation angle to model work, especially around user interaction, multilingual language behavior, and how LLM performance gets tested in practice.

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Gal Shachaf

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Worth knowing because his work links earlier dense-retrieval research to later MRKL and Jamba systems, which makes his page a good bridge between classic NLP retrieval and newer hybrid LLM stacks.

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Hofit Bata

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A useful page because it points to the research-and-strategy side of AI21 rather than only the product or engineering side, especially where model evaluation and new architectural bets get shaped at the CTO-office level.

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