Yejin Choi

About

Yejin Choi is a leading figure in AI research, focusing on AI for Science, Pluralistic Alignment & AI for humanity, and Alternative Training and Inference Algorithms. She works on molecular foundation models, protein reasoning, and molecular reasoning and retrosynthesis. She also explores data and algorithms for pluralistic norms and values, deliberate alignment processes, and civic discourse.

Research topics

• Artificial Intelligence
• Computer Science
• Natural Language Processing
• Data Mining
• Information Retrieval
• Programming language
• Computer vision
• Chemistry
• Linguistics
• Chromatography

Selected publications

• Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models

  arXiv (Cornell University) · 2022 · 548 citations

  Language models demonstrate both quantitative improvement and new qualitative capabilities with increasing scale. Despite their potentially transformative impact, these new capabilities are as yet poorly characterized. In order to inform future research, prepare for disruptive new model capabilities, and ameliorate socially harmful effects, it is vital that we understand the present and near-future capabilities and limitations of language models. To address this challenge, we introduce the Beyon…

  DOI

• The Curious Case of Neural Text Degeneration

  arXiv (Cornell University) · 2020 · 528 citations

  Senior authorCorresponding

  Despite considerable advances in neural language modeling, it remains an open question what the best decoding strategy is for text generation from a language model (e.g. to generate a story). The counter-intuitive empirical observation is that even though the use of likelihood as training objective leads to high quality models for a broad range of language understanding tasks, maximization-based decoding methods such as beam search lead to degeneration — output text that is bland, incoherent, or…

• MAUVE: Measuring the Gap Between Neural Text and Human Text using\n Divergence Frontiers

  arXiv (Cornell University) · 2021 · 91 citations

  As major progress is made in open-ended text generation, measuring how close\nmachine-generated text is to human language remains a critical open problem. We\nintroduce MAUVE, a comparison measure for open-ended text generation, which\ndirectly compares the learnt distribution from a text generation model to the\ndistribution of human-written text using divergence frontiers. MAUVE scales up\nto modern text generation models by computing information divergences in a\nquantized embedding space. Th…

  DOI

• HALoGEN: Fantastic LLM Hallucinations and Where to Find Them

  2025-01-01 · 5 citations

  articleOpen accessSenior author

  Despite their impressive ability to generate high-quality and fluent text, generative large language models (LLMs) also produce hallucinations: statements that are misaligned with established world knowledge or provided input context.However, measuring hallucination can be challenging, as having humans verify model generations on-the-fly is both expensive and time-consuming.In this work, we release HALOGEN , a comprehensive hallucination benchmark consisting of: (1) 10,923 prompts for generative…

  PublisherOA PDFDOI

• HALoGEN: Fantastic LLM Hallucinations and Where to Find Them

  ArXiv.org · 2025-01-14 · 3 citations

  preprintOpen accessSenior author

  Despite their impressive ability to generate high-quality and fluent text, generative large language models (LLMs) also produce hallucinations: statements that are misaligned with established world knowledge or provided input context. However, measuring hallucination can be challenging, as having humans verify model generations on-the-fly is both expensive and time-consuming. In this work, we release HALoGEN, a comprehensive hallucination benchmark consisting of: (1) 10,923 prompts for generativ…

  PublisherOA PDFDOI

Recent grants

• RI: Small: ConnotationNet: Modeling Non-Literal Meaning in Context

  NSF · $500k · 2017–2021

• RI: Small: A Data-Driven Framework to Sketch-to-Text Generation

  NSF · $450k · 2015–2019

Frequent coauthors

• Ronan Le Bras

  186 shared

• Maarten Sap

  153 shared

• Noah A. Smith

  137 shared

• Chandra Bhagavatula

  Allen Institute

  126 shared

• Ximing Lu

  117 shared

• Swabha Swayamdipta

  95 shared

• Antoine Bosselut

  86 shared

• Jack Hessel

  86 shared

Awards & honors

• MacArthur Fellow (class of 2022)
• ACL Fellow (2022)
• Brett Helsel Career Development Professorship (2020 - 2023)
• Borg Early Career Award (BECA) (2018)
• IEEE AI's 10 to Watch (2016)

Similar researchers at Stanford University

• Percy Liangh-93
• Carlos Guestrinh-86
• Stefano Ermonh-66