3 sources
A strong profile for the line from classic semantic parsing into modern tool use, retrieval, and language-model adaptation at scale.
Start HereLuke Zettlemoyer - AI at Meta
Researcher Profile
Editor reviewedAri Holtzman
Efficient finetuning of quantized LLMs
Assistant professor at the University of Chicago working on generation and evaluation
Important because he helped define how people think about language-model decoding quality, and his work keeps showing up where practical generation behavior matters more than benchmark theater.
Organizations
University of Chicago
About This Page
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Last reviewed
March 20, 2026
Official And External Links
Known For
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01
Nucleus sampling
02
Generation quality and degeneration analysis
03
QLoRA-era practical finetuning work
04
Efficient finetuning of quantized LLMs
05
QLoRA: Efficient Finetuning of Quantized LLMs
06
QLoRA
Start Here
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Related Researchers
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3 sources
A core person to know for making serious language-model finetuning and inference feasible on smaller hardware, especially through quantization and optimizer tooling that working builders actually use.
Start HereAbout Me — Tim Dettmers
1 source
Co-authored QLoRA: made high-quality fine-tuning feasible on modest hardware.
4 sources
A good person to follow if you care about what deployment-minded safety work looks like inside a frontier lab, especially around moderation, image systems, and system-card style evaluation.
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A useful person to follow if you want to understand the engineering side of frontier language models, especially the line running from Codex and GPT-style systems into later open-weight and product-facing deployments.
3 sources
A strong person to follow if you care about open-weight language models and retrieval-heavy NLP systems, especially the line from RoBERTa and RAG into LLaMA-era model development.
Start HereThinking Machines Lab