Anastasiia Razdaibiedina — accepted AI-conference papers

2023

MIReAD: Simple Method for Learning High-quality Representations from Scientific Documents

Anastasiia Razdaibiedina, Aleksandr Brechalov

ACL 2023short

Learning semantically meaningful representations from scientific documents can facilitate academic literature search and improve performance of recommendation systems. Pretrained language models have been shown to learn rich textual representations, yet they cannot provide powerful document-level re…

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2023

Residual Prompt Tuning: improving prompt tuning with residual reparameterization

Anastasiia Razdaibiedina, Yuning Mao, Madian Khabsa, Mike Lewis, Rui Hou, Jimmy Ba +1

ACL 2023findings

Prompt tuning is one of the successful approaches for parameter-efficient tuning of pre-trained language models. Despite being arguably the most parameter-efficient (tuned soft prompts constitute <0.1% of total parameters), it typically performs worse than other efficient tuning methods and is quite…

Cited by 47 Source ↗PDF ↗code ↗Scholar ↗