1 citations · 1 across the 3 of their papers we have counts for
4 papers
Struct-Bench: A Benchmark for Differentially Private Structured Text Generation
Shuaiqi Wang, Vikas Raunak, Arturs Backurs +7
Differentially private (DP) synthetic data generation is a promising technique for utilizing private datasets that otherwise cannot be exposed for model training or other analytics…
On Instruction-Finetuning Neural Machine Translation Models
Vikas Raunak, Roman Grundkiewicz, Marcin Junczys-Dowmunt
In this work, we introduce instruction finetuning for Neural Machine Translation (NMT) models, which distills instruction following capabilities from Large Language Models (LLMs) i…
Dissecting In-Context Learning of Translations in GPTs
Vikas Raunak, Hany Hassan Awadalla, Arul Menezes
Most of the recent work in leveraging Large Language Models (LLMs) such as GPT-3 for Machine Translation (MT) has focused on selecting the few-shot samples for prompting. In this w…
SLIDE: Reference-free Evaluation for Machine Translation using a Sliding Document Window
Vikas Raunak, Tom Kocmi, Matt Post
Reference-based metrics that operate at the sentence-level typically outperform quality estimation metrics, which have access only to the source and system output. This is unsurpri…