2 citations · 2 across the 2 of their papers we have counts for
3 papers
cs.CL2026
Universal NER v2: Towards a Massively Multilingual Named Entity Recognition Benchmark
Terra Blevins, Stephen Mayhew, Marek Šuppa +11
While multilingual language models promise to bring the benefits of LLMs to speakers of many languages, gold-standard evaluation benchmarks in most languages to interrogate these a…
cs.CL2024★ 2 cited
From Tarzan to Tolkien: Controlling the Language Proficiency Level of LLMs for Content Generation
Ali Malik, Stephen Mayhew, Chris Piech +1
We study the problem of controlling the difficulty level of text generated by Large Language Models (LLMs) for contexts where end-users are not fully proficient, such as language l…
cs.CL2023
Universal NER: A Gold-Standard Multilingual Named Entity Recognition Benchmark
Stephen Mayhew, Terra Blevins, Shuheng Liu +10
We introduce Universal NER (UNER), an open, community-driven project to develop gold-standard NER benchmarks in many languages. The overarching goal of UNER is to provide high-qual…