Language Models: A Guide for the Perplexed
Not on the map. Why?
Abstract (arXiv)
Given the growing importance of AI literacy, we decided to write this tutorial to help narrow the gap between the discourse among those who study language models -- the core technology underlying ChatGPT and similar products -- and those who are intrigued and want to learn more about them. In short, we believe the perspective of researchers and educators can add some clarity to the public's understanding of the technologies beyond what's currently available, which tends to be either extremely technical or promotional material generated about products by their purveyors. Our approach teases apart the concept of a language model from products built on them, from the behaviors attributed to or desired from those products, and from claims about similarity to human cognition. As a starting point, we (1) offer a scientific viewpoint that focuses on questions amenable to study through experimentation; (2) situate language models as they are today in the context of the research that led to their development; and (3) describe the boundaries of what is known about the models at this writing.
Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 6 of its 16 references that are in the map. First posted on arXiv 29 Nov 2023.
Builds on
6 of 16 references.
References · 6
- 2017Attention Is All You Needcited by 60,361
- 2015Neural Machine Translation of Rare Words with Subword Unitscited by 2,918
- 2018Deep contextualized word representationscited by 2,290
- 2021Documenting Large Webtext Corpora: A Case Study on the Colossal Clean Crawled Corpuscited by 492
- 2019Mitigating Bias in Algorithmic Hiring: Evaluating Claims and Practicescited by 174
- 2018Assessing Gender Bias in Machine Translation -- A Case Study with Google Translatecited by 121
Built on by
Citing papers per year: 2024: 1; 2025: 1.
2 papers.