2.1k citations · 2.8k across the 17 of their papers we have counts for
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Understanding the Dataset Practitioners Behind Large Language Model Development
Crystal Qian, Emily Reif, Minsuk Kahng
As large language models (LLMs) become more advanced and impactful, it is increasingly important to scrutinize the data that they rely upon and produce. What is it to be a dataset…
Automatic Histograms: Leveraging Language Models for Text Dataset Exploration
Emily Reif, Crystal Qian, James Wexler +1
Making sense of unstructured text datasets is perennially difficult, yet increasingly relevant with Large Language Models. Data workers often rely on dataset summaries, especially…
A Pretrainer's Guide to Training Data: Measuring the Effects of Data Age, Domain Coverage, Quality, & Toxicity
Shayne Longpre, Gregory Yauney, Emily Reif +8
Pretraining is the preliminary and fundamental step in developing capable language models (LM). Despite this, pretraining data design is critically under-documented and often guide…
Visualizing Linguistic Diversity of Text Datasets Synthesized by Large Language Models
Emily Reif, Minsuk Kahng, Savvas Petridis
Large language models (LLMs) can be used to generate smaller, more refined datasets via few-shot prompting for benchmarking, fine-tuning or other use cases. However, understanding…
PaLM 2 Technical Report
Rohan Anil, Andrew M. Dai, Orhan Firat +125
We introduce PaLM 2, a new state-of-the-art language model that has better multilingual and reasoning capabilities and is more compute-efficient than its predecessor PaLM. PaLM 2 i…
The Case for a Single Model that can Both Generate Continuations and Fill in the Blank
Daphne Ippolito, Liam Dugan, Emily Reif +3
The task of inserting text into a specified position in a passage, known as fill in the blank (FitB), is useful for a variety of applications where writers interact with a natural…
PaLM: Scaling Language Modeling with Pathways
Aakanksha Chowdhery, Sharan Narang, Jacob Devlin +64
Large language models have been shown to achieve remarkable performance across a variety of natural language tasks using few-shot learning, which drastically reduces the number of…
A Recipe For Arbitrary Text Style Transfer with Large Language Models
Emily Reif, Daphne Ippolito, Ann Yuan +3
In this paper, we leverage large language models (LMs) to perform zero-shot text style transfer. We present a prompting method that we call augmented zero-shot learning, which fram…
Wordcraft: a Human-AI Collaborative Editor for Story Writing
Andy Coenen, Luke Davis, Daphne Ippolito +2
As neural language models grow in effectiveness, they are increasingly being applied in real-world settings. However these applications tend to be limited in the modes of interacti…
An Interpretability Illusion for BERT
Tolga Bolukbasi, Adam Pearce, Ann Yuan +4
We describe an "interpretability illusion" that arises when analyzing the BERT model. Activations of individual neurons in the network may spuriously appear to encode a single, sim…
The Language Interpretability Tool: Extensible, Interactive Visualizations and Analysis for NLP Models
Ian Tenney, James Wexler, Jasmijn Bastings +8
We present the Language Interpretability Tool (LIT), an open-source platform for visualization and understanding of NLP models. We focus on core questions about model behavior: Why…