research
∙
09/07/2023
DoLa: Decoding by Contrasting Layers Improves Factuality in Large Language Models
Despite their impressive capabilities, large language models (LLMs) are ...
share
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...
research
∙
05/26/2023
Entailment as Robust Self-Learner
Entailment has been recognized as an important metric for evaluating nat...
research
∙
05/24/2023
Deriving Language Models from Masked Language Models
Masked language models (MLM) do not explicitly define a distribution ove...
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 ...
research
∙
03/02/2023
Learning to Grow Pretrained Models for Efficient Transformer Training
Scaling transformers has led to significant breakthroughs in many domain...
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...
research
∙
12/18/2022
Unsupervised Discontinuous Constituency Parsing with Mildly Context-Sensitive Grammars
We study grammar induction with mildly context-sensitive grammars for un...
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...
research
∙
11/17/2022
Probing for Incremental Parse States in Autoregressive Language Models
Next-word predictions from autoregressive neural language models show re...
research
∙
11/15/2022
Hierarchical Phrase-based Sequence-to-Sequence Learning
We describe a neural transducer that maintains the flexibility of standa...
research
∙
05/31/2022
VALHALLA: Visual Hallucination for Machine Translation
Designing better machine translation systems by considering auxiliary in...
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-...
research
∙
05/03/2022
Inducing and Using Alignments for Transition-based AMR Parsing
Transition-based parsers for Abstract Meaning Representation (AMR) rely ...
research
∙
03/02/2022
Controlling the Focus of Pretrained Language Generation Models
The finetuning of pretrained transformer-based language generation model...
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...
research
∙
09/02/2021
Sequence-to-Sequence Learning with Latent Neural Grammars
Sequence-to-sequence learning with neural networks has become the de fac...
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...
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...
research
∙
12/14/2020
Parameter-Efficient Transfer Learning with Diff Pruning
While task-specific finetuning of pretrained networks has led to signifi...
research
∙
11/18/2020
Sequence-Level Mixed Sample Data Augmentation
Despite their empirical success, neural networks still have difficulty c...
research
∙
06/01/2020
Emergence of Separable Manifolds in Deep Language Representations
Deep neural networks (DNNs) have shown much empirical success in solving...
research
∙
06/24/2019
Compound Probabilistic Context-Free Grammars for Grammar Induction
We study a formalization of the grammar induction problem that models se...
research
∙
06/14/2019
Amortized Bethe Free Energy Minimization for Learning MRFs
We propose to learn deep undirected graphical models (i.e., MRFs), with ...
research
∙
04/07/2019
Unsupervised Recurrent Neural Network Grammars
Recurrent neural network grammars (RNNG) are generative models of langua...
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...
research
∙
07/12/2018
Avoiding Latent Variable Collapse With Generative Skip Models
Variational autoencoders (VAEs) learn distributions of high-dimensional ...
research
∙
07/10/2018
Latent Alignment and Variational Attention
Neural attention has become central to many state-of-the-art models in n...
research
∙
05/28/2018
OpenNMT: Neural Machine Translation Toolkit
OpenNMT is an open-source toolkit for neural machine translation (NMT). ...
research
∙
02/07/2018
Semi-Amortized Variational Autoencoders
Amortized variational inference (AVI) replaces instance-specific local i...
research
∙
07/27/2017
Adapting Sequence Models for Sentence Correction
In a controlled experiment of sequence-to-sequence approaches for the ta...
research
∙
06/13/2017
Adversarially Regularized Autoencoders
While autoencoders are a key technique in representation learning for co...
research
∙
02/03/2017
Structured Attention Networks
Attention networks have proven to be an effective approach for embedding...
research
∙
01/10/2017
OpenNMT: Open-Source Toolkit for Neural Machine Translation
We describe an open-source toolkit for neural machine translation (NMT)....
research
∙
06/25/2016
Sequence-Level Knowledge Distillation
Neural machine translation (NMT) offers a novel alternative formulation ...
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...
research
∙
08/26/2015
Character-Aware Neural Language Models
We describe a simple neural language model that relies only on character...
research
∙
08/25/2014
