Yiyun Li

About

Yiyun Li is the Robert F. Goheen Professor in the Humanities and a Professor of Creative Writing at Princeton University. She is an acclaimed author whose works include the memoir Things in Nature Merely Grow, which won the 2026 Pulitzer Prize for memoir, as well as other notable books such as Wednesday's Child, The Book of Goose, Where Reasons End, Dear Friend, from My Life I Write to You in Your Life, and Tolstoy Together, 85 Days of War and Peace with Yiyun Li. Her work has been translated into more than twenty languages and has received numerous honors and awards, including a MacArthur Foundation Fellowship, a Guggenheim Fellowship, a Windham Campbell Prize, the Andrew Carnegie Medal, and the International Writer Award from the Royal Society of Literature. She is a member of both the American Academy of Arts and Sciences and the American Academy of Arts and Letters, and her literary contributions extend into film, with her short story adapted into the award-winning film A Thousand Years of Good Prayers. Li's research and creative work focus on contemporary fiction, literary storytelling, and the exploration of human experiences through her writing.

Research topics

• Computer Science
• Artificial Intelligence
• Machine Learning
• Mathematical optimization
• Mathematics
• Parallel computing
• Mathematical analysis
• Applied mathematics
• Statistics
• Algorithm

Selected publications

• LoRA: Low-Rank Adaptation of Large Language Models

  arXiv (Cornell University) · 2021 · 2415 citations

  An important paradigm of natural language processing consists of large-scale pre-training on general domain data and adaptation to particular tasks or domains. As we pre-train larger models, full fine-tuning, which retrains all model parameters, becomes less feasible. Using GPT-3 175B as an example -- deploying independent instances of fine-tuned models, each with 175B parameters, is prohibitively expensive. We propose Low-Rank Adaptation, or LoRA, which freezes the pre-trained model weights and…

  DOI

• Phi-3 Technical Report: A Highly Capable Language Model Locally on Your Phone

  arXiv (Cornell University) · 2024-04-22 · 151 citations

  preprintOpen access

  We introduce phi-3-mini, a 3.8 billion parameter language model trained on 3.3 trillion tokens, whose overall performance, as measured by both academic benchmarks and internal testing, rivals that of models such as Mixtral 8x7B and GPT-3.5 (e.g., phi-3-mini achieves 69% on MMLU and 8.38 on MT-bench), despite being small enough to be deployed on a phone. Our training dataset is a scaled-up version of the one used for phi-2, composed of heavily filtered publicly available web data and synthetic da…

  PublisherOA PDFDOI

• Physics of Language Models: Part 3.3, Knowledge Capacity Scaling Laws

  arXiv (Cornell University) · 2024-04-08 · 4 citations

  preprintOpen accessSenior author

  Scaling laws describe the relationship between the size of language models and their capabilities. Unlike prior studies that evaluate a model's capability via loss or benchmarks, we estimate the number of knowledge bits a model stores. We focus on factual knowledge represented as tuples, such as (USA, capital, Washington D.C.) from a Wikipedia page. Through multiple controlled datasets, we establish that language models can and only can store 2 bits of knowledge per parameter, even when quantize…

  PublisherOA PDFDOI

• Partisan Politics and Annual Shareholder Meeting Formats

  National Bureau of Economic Research · 2024-07-01 · 1 citations

  reportOpen access1st authorCorresponding

  We study companies' decisions about holding annual shareholder meetings on-line during the Covid pandemic, and returning to classical in-person meetings post-pandemic.Among S&P 1500 companies, the frequency of virtual meetings shot up from less than 10 percent to more than 80 percent in the first year of the pandemic, with only gradual reversion to in-person meetings since then.Partisan politics has significant associations with these decisions.In-person meetings are more likely for companies th…

  PublisherDOI

• Mixture of Parrots: Experts improve memorization more than reasoning

  arXiv (Cornell University) · 2024-10-24 · 1 citations

  preprintOpen access

  The Mixture-of-Experts (MoE) architecture enables a significant increase in the total number of model parameters with minimal computational overhead. However, it is not clear what performance tradeoffs, if any, exist between MoEs and standard dense transformers. In this paper, we show that as we increase the number of experts (while fixing the number of active parameters), the memorization performance consistently increases while the reasoning capabilities saturate. We begin by analyzing the the…

  PublisherOA PDFDOI

Frequent coauthors

• Zeyuan Allen-Zhu

  44 shared

• Sébastien Bubeck

  33 shared

• Yingyu Liang

  22 shared

• Yin Tat Lee

  21 shared

• Mark Sellke

  Harvard University

  21 shared

• Tengyu Ma

  18 shared

• Andrej Risteski

  17 shared

• Sanjeev Arora

  9 shared

Labs

• Yiyun Li's Creative Writing LabPI

Awards & honors

• MacArthur Foundation Fellowship
• Guggenheim Fellowship
• Windham Campbell Prize
• 2026 Andrew Carnegie Medal
• 2021 Literature Award from the American Academy of Arts and…

Similar researchers at Princeton University

• Patricia Smithh-13