14 citations · 21 across the 5 of their papers we have counts for
4 papers · 1 filter
A Benchmark for Learning to Translate a New Language from One Grammar Book
Garrett Tanzer, Mirac Suzgun, Eline Visser +2
Large language models (LLMs) can perform impressive feats with in-context learning or lightweight finetuning. It is natural to wonder how well these models adapt to genuinely new t…
Follow the Wisdom of the Crowd: Effective Text Generation via Minimum Bayes Risk Decoding
Mirac Suzgun, Luke Melas-Kyriazi, Dan Jurafsky
In open-ended natural-language generation, existing text decoding methods typically struggle to produce text which is both diverse and high-quality. Greedy and beam search are know…
The Harvard USPTO Patent Dataset: A Large-Scale, Well-Structured, and Multi-Purpose Corpus of Patent Applications
Mirac Suzgun, Luke Melas-Kyriazi, Suproteem K. Sarkar +2
Innovation is a major driver of economic and social development, and information about many kinds of innovation is embedded in semi-structured data from patents and patent applicat…
Prompt-and-Rerank: A Method for Zero-Shot and Few-Shot Arbitrary Textual Style Transfer with Small Language Models
Mirac Suzgun, Luke Melas-Kyriazi, Dan Jurafsky
We propose a method for arbitrary textual style transfer (TST)--the task of transforming a text into any given style--utilizing general-purpose pre-trained language models. Our met…