Emily Bender 简介：
Emily M. Bender (born 1973) is an American linguist who is a professor at the University of Washington. She specializes in computational linguistics and natural language processing. She is also the director of the University of Washington’s Computational Linguistics Laboratory. She has published several papers on the risks of large language models. (编者注：Bender入选9月7日上线的《时代周刊（TIME）》“2023年人工智能100人”，是其中为数不多的语言学家)
Bender has published research papers on the linguistic structures of Japanese, Chintang, Mandarin, Wambaya, American Sign Language and English.
Bender has constructed the LinGO Grammar Matrix, an open-source starter kit for the development of broad-coverage precision HPSG grammars.In 2013, she published Linguistic Fundamentals for Natural Language Processing: 100 Essentials from Morphology and Syntax, and in 2019, she published Linguistic Fundamentals for Natural Language Processing II: 100 Essentials from Semantics and Pragmatics with Alex Lascarides, which both explain basic linguistic principles in a way that makes them accessible to NLP practitioners.
In 2021, Bender presented a paper, “On the Dangers of Stochastic Parrots: Can Language Models Be Too Big? 🦜” co-authored with Google researcher Timnit Gebru and others at the ACM Conference on Fairness, Accountability, and Transparency that Google tried to block from publication, part of a sequence of events leading to Gebru departing from Google, the details of which are disputed. The paper concerned ethical issues in building natural language processing systems using machine learning from large text corpora. Since then, she has invested efforts to popularize AI ethics and has taken a stand against hype over large language models.
The Bender Rule, which originated from the question Bender repeatedly asked at the research talks, is research advice for computational scholars to “always name the language you’re working with”.
She draws a distinction between linguistic form versus linguistic meaning. Form refers to the structure of language (e.g. syntax), whereas meaning refers to the ideas that language represents. In a 2020 paper, she argued that machine learning models for natural language processing which are trained only on form, without connection to meaning, cannot meaningfully understand language. Therefore, she has argued that tools like ChatGPT have no way to meaningfully understand the text that they process, nor the text that they generate.