Search Results for author:
Found 17 papers, 12 papers with code
Directly Fine-Tuning Diffusion Models on Differentiable Rewards
We present Direct Reward Fine-Tuning (DRaFT), a simple and effective method for fine-tuning diffusion models to maximize differentiable reward functions, such as scores from human preference models.
Intriguing properties of generative classifiers
What is the best paradigm to recognize objects -- discriminative inference (fast but potentially prone to shortcut learning) or using a generative model (slow but potentially more robust)?
Ranked #1 on
Object Recognition
on shape bias
Towards Expert-Level Medical Question Answering with Large Language Models
Large language models (LLMs) have catalyzed significant progress in medical question answering; Med-PaLM was the first model to exceed a "passing" score in US Medical Licensing Examination (USMLE) style questions with a score of 67. 2% on the MedQA dataset.
Text-to-Image Diffusion Models are Zero-Shot Classifiers
The key idea is using a diffusion model's ability to denoise a noised image given a text description of a label as a proxy for that label's likelihood.
Meta-Learning Fast Weight Language Models
Dynamic evaluation of language models (LMs) adapts model parameters at test time using gradient information from previous tokens and substantially improves LM performance.
Pre-Training Transformers as Energy-Based Cloze Models
We introduce Electric, an energy-based cloze model for representation learning over text.
ELECTRA: Pre-training Text Encoders as Discriminators Rather Than Generators
Then, instead of training a model that predicts the original identities of the corrupted tokens, we train a discriminative model that predicts whether each token in the corrupted input was replaced by a generator sample or not.
Ranked #7 on
Question Answering
on Quora Question Pairs
BAM! Born-Again Multi-Task Networks for Natural Language Understanding
It can be challenging to train multi-task neural networks that outperform or even match their single-task counterparts.
What Does BERT Look At? An Analysis of BERT's Attention
Large pre-trained neural networks such as BERT have had great recent success in NLP, motivating a growing body of research investigating what aspects of language they are able to learn from unlabeled data.
Sample Efficient Text Summarization Using a Single Pre-Trained Transformer
Language model (LM) pre-training has resulted in impressive performance and sample efficiency on a variety of language understanding tasks.
Ranked #1 on
Text Summarization
on DUC 2004 Task 1
(ROUGE-2 metric)
Semi-Supervised Sequence Modeling with Cross-View Training
We therefore propose Cross-View Training (CVT), a semi-supervised learning algorithm that improves the representations of a Bi-LSTM sentence encoder using a mix of labeled and unlabeled data.
Ranked #3 on
CCG Supertagging
on CCGbank
Cross-View Training for Semi-Supervised Learning
The students can learn from the teacher (the full model) because the teacher sees more of each example.
Ranked #4 on
Chunking
on CoNLL 2000
(using extra training data)
Deep Reinforcement Learning for Mention-Ranking Coreference Models
Coreference resolution systems are typically trained with heuristic loss functions that require careful tuning.
Ranked #24 on
Coreference Resolution
on OntoNotes
Inducing Domain-Specific Sentiment Lexicons from Unlabeled Corpora
A word's sentiment depends on the domain in which it is used.
Improving Coreference Resolution by Learning Entity-Level Distributed Representations
A long-standing challenge in coreference resolution has been the incorporation of entity-level information - features defined over clusters of mentions instead of mention pairs.
Ranked #25 on
Coreference Resolution
on OntoNotes
Large-scale Analysis of Counseling Conversations: An Application of Natural Language Processing to Mental Health
Mental illness is one of the most pressing public health issues of our time.
