b'Yoon Kim'

research

∙ 09/07/2023

DoLa: Decoding by Contrasting Layers Improves Factuality in Large Language Models

Despite their impressive capabilities, large language models (LLMs) are ...

0 Yung-Sung Chuang, et al. ∙

research

∙ 05/30/2023

Grammar Prompting for Domain-Specific Language Generation with Large Language Models

Large language models (LLMs) can learn to perform a wide range of natura...

0 Bailin Wang, et al. ∙

research

∙ 05/26/2023

Entailment as Robust Self-Learner

Entailment has been recognized as an important metric for evaluating nat...

0 Jiaxin Ge, et al. ∙

research

∙ 05/24/2023

Deriving Language Models from Masked Language Models

Masked language models (MLM) do not explicitly define a distribution ove...

0 Lucas Torroba Hennigen, et al. ∙

research

∙ 03/06/2023

Multitask Prompt Tuning Enables Parameter-Efficient Transfer Learning

Prompt tuning, in which a base pretrained model is adapted to each task ...

0 Zhen Wang, et al. ∙

research

∙ 03/02/2023

Learning to Grow Pretrained Models for Efficient Transformer Training

Scaling transformers has led to significant breakthroughs in many domain...

0 Peihao Wang, et al. ∙

research

∙ 02/08/2023

Federated Learning as Variational Inference: A Scalable Expectation Propagation Approach

The canonical formulation of federated learning treats it as a distribut...

0 Han Guo, et al. ∙

research

∙ 12/18/2022

Unsupervised Discontinuous Constituency Parsing with Mildly Context-Sensitive Grammars

We study grammar induction with mildly context-sensitive grammars for un...

0 Songlin Yang, et al. ∙

research

∙ 11/20/2022

Aging with GRACE: Lifelong Model Editing with Discrete Key-Value Adaptors

Large pre-trained models decay over long-term deployment as input distri...

0 Thomas Hartvigsen, et al. ∙

research

∙ 11/17/2022

Probing for Incremental Parse States in Autoregressive Language Models

Next-word predictions from autoregressive neural language models show re...

0 Tiwalayo Eisape, et al. ∙

research

∙ 11/15/2022

Hierarchical Phrase-based Sequence-to-Sequence Learning

We describe a neural transducer that maintains the flexibility of standa...

0 Bailin Wang, et al. ∙

research

∙ 05/31/2022

VALHALLA: Visual Hallucination for Machine Translation

Designing better machine translation systems by considering auxiliary in...

0 Yi Li, et al. ∙

research

∙ 05/25/2022

Large Language Models are Zero-Shot Clinical Information Extractors

We show that large language models, such as GPT-3, perform well at zero-...

0 Monica Agrawal, et al. ∙

research

∙ 05/03/2022

Inducing and Using Alignments for Transition-based AMR Parsing

Transition-based parsers for Abstract Meaning Representation (AMR) rely ...

2 Andrew Drozdov, et al. ∙

research

∙ 03/02/2022

Controlling the Focus of Pretrained Language Generation Models

The finetuning of pretrained transformer-based language generation model...

0 Jiabao Ji, et al. ∙

research

∙ 02/02/2022

Co-training Improves Prompt-based Learning for Large Language Models

We demonstrate that co-training (Blum Mitchell, 1998) can improve th...

0 Hunter Lang, et al. ∙

research

∙ 09/02/2021

Sequence-to-Sequence Learning with Latent Neural Grammars

Sequence-to-sequence learning with neural networks has become the de fac...

0 Yoon Kim, et al. ∙

research

∙ 07/13/2021

Developmental Stage Classification of Embryos Using Two-Stream Neural Network with Linear-Chain Conditional Random Field

The developmental process of embryos follows a monotonic order. An embry...

10 Stanislav Lukyanenko, et al. ∙

research

∙ 04/15/2021

Syntactic Perturbations Reveal Representational Correlates of Hierarchical Phrase Structure in Pretrained Language Models

While vector-based language representations from pretrained language mod...

0 Matteo Alleman, et al. ∙

research

∙ 12/14/2020

Parameter-Efficient Transfer Learning with Diff Pruning

While task-specific finetuning of pretrained networks has led to signifi...

0 Demi Guo, et al. ∙

research

∙ 11/18/2020

Sequence-Level Mixed Sample Data Augmentation

Despite their empirical success, neural networks still have difficulty c...

0 Demi Guo, et al. ∙

research

∙ 06/01/2020

Emergence of Separable Manifolds in Deep Language Representations

Deep neural networks (DNNs) have shown much empirical success in solving...

0 Jonathan Mamou, et al. ∙

research

∙ 06/24/2019

Compound Probabilistic Context-Free Grammars for Grammar Induction

We study a formalization of the grammar induction problem that models se...

0 Yoon Kim, et al. ∙

research

∙ 06/14/2019

Amortized Bethe Free Energy Minimization for Learning MRFs

We propose to learn deep undirected graphical models (i.e., MRFs), with ...

0 Sam Wiseman, et al. ∙

research

∙ 04/07/2019

Unsupervised Recurrent Neural Network Grammars

Recurrent neural network grammars (RNNG) are generative models of langua...

0 Yoon Kim, et al. ∙

research

∙ 12/17/2018

A Tutorial on Deep Latent Variable Models of Natural Language

There has been much recent, exciting work on combining the complementary...

0 Yoon Kim, et al. ∙

research

∙ 07/12/2018

Avoiding Latent Variable Collapse With Generative Skip Models

Variational autoencoders (VAEs) learn distributions of high-dimensional ...

0 Adji B. Dieng, et al. ∙

research

∙ 07/10/2018

Latent Alignment and Variational Attention

Neural attention has become central to many state-of-the-art models in n...

0 Yuntian Deng, et al. ∙

research

∙ 05/28/2018

OpenNMT: Neural Machine Translation Toolkit

OpenNMT is an open-source toolkit for neural machine translation (NMT). ...

0 Guillaume Klein, et al. ∙

research

∙ 02/07/2018

Semi-Amortized Variational Autoencoders

Amortized variational inference (AVI) replaces instance-specific local i...

0 Yoon Kim, et al. ∙

research

∙ 07/27/2017

Adapting Sequence Models for Sentence Correction

In a controlled experiment of sequence-to-sequence approaches for the ta...

0 Allen Schmaltz, et al. ∙

research

∙ 06/13/2017

Adversarially Regularized Autoencoders

While autoencoders are a key technique in representation learning for co...

0 Junbo, et al. ∙

research

∙ 02/03/2017

Structured Attention Networks

Attention networks have proven to be an effective approach for embedding...

0 Yoon Kim, et al. ∙

research

∙ 01/10/2017

OpenNMT: Open-Source Toolkit for Neural Machine Translation

We describe an open-source toolkit for neural machine translation (NMT)....

0 Guillaume Klein, et al. ∙

research

∙ 06/25/2016

Sequence-Level Knowledge Distillation

Neural machine translation (NMT) offers a novel alternative formulation ...

0 Yoon Kim, et al. ∙

research

∙ 04/16/2016

Sentence-Level Grammatical Error Identification as Sequence-to-Sequence Correction

We demonstrate that an attention-based encoder-decoder model can be used...

0 Allen Schmaltz, et al. ∙

research

∙ 08/26/2015

Character-Aware Neural Language Models

We describe a simple neural language model that relies only on character...

0 Yoon Kim, et al. ∙

research

∙ 08/25/2014

Convolutional Neural Networks for Sentence Classification

We report on a series of experiments with convolutional neural networks ...

0 Yoon Kim, et al. ∙

Yoon Kim

Featured Co-authors