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Found 22 papers, 11 papers with code
GPT-4 Technical Report
We report the development of GPT-4, a large-scale, multimodal model which can accept image and text inputs and produce text outputs.
Ranked #1 on
Question Answering
on PeerQA
Text and Code Embeddings by Contrastive Pre-Training
Similarly to text embeddings, we train code embedding models on (text, code) pairs, obtaining a 20. 8% relative improvement over prior best work on code search.
Ranked #1 on
Passage Ranking
on MS MARCO
Unsupervised Neural Machine Translation with Generative Language Models Only
We show how to derive state-of-the-art unsupervised neural machine translation systems from generatively pre-trained language models.
On Task-Level Dialogue Composition of Generative Transformer Model
In this work, we begin by studying the effect of training human-human task-oriented dialogues towards improving the ability to compose multiple tasks on Transformer generative models.
Language Models are Few-Shot Learners
By contrast, humans can generally perform a new language task from only a few examples or from simple instructions - something which current NLP systems still largely struggle to do.
Ranked #1 on
Question Answering
on CoQA
(Overall metric)
Trading Off Diversity and Quality in Natural Language Generation
For open-ended language generation tasks such as storytelling and dialogue, choosing the right decoding algorithm is critical to controlling the tradeoff between generation quality and diversity.
Neural Assistant: Joint Action Prediction, Response Generation, and Latent Knowledge Reasoning
In this paper, we develop Neural Assistant: a single neural network model that takes conversation history and an external knowledge source as input and jointly produces both text response and action to be taken by the system as output.
Taskmaster-1: Toward a Realistic and Diverse Dialog Dataset
A significant barrier to progress in data-driven approaches to building dialog systems is the lack of high quality, goal-oriented conversational data.
Parallel Scheduled Sampling
Experimentally, we find the proposed technique leads to equivalent or better performance on image generation, summarization, dialog generation, and translation compared to teacher-forced training.
Towards a better understanding of Vector Quantized Autoencoders
Deep neural networks with discrete latent variables offer the promise of better symbolic reasoning, and learning abstractions that are more useful to new tasks.
Theory and Experiments on Vector Quantized Autoencoders
Deep neural networks with discrete latent variables offer the promise of better symbolic reasoning, and learning abstractions that are more useful to new tasks.
RelNet: End-to-End Modeling of Entities & Relations
We introduce RelNet: a new model for relational reasoning.
Learning a Natural Language Interface with Neural Programmer
The main experimental result in this paper is that a single Neural Programmer model achieves 34. 2% accuracy using only 10, 000 examples with weak supervision.
Chains of Reasoning over Entities, Relations, and Text using Recurrent Neural Networks
Our goal is to combine the rich multistep inference of symbolic logical reasoning with the generalization capabilities of neural networks.
Generalizing to Unseen Entities and Entity Pairs with Row-less Universal Schema
In experiments predicting both relations and entity types, we demonstrate that despite having an order of magnitude fewer parameters than traditional universal schema, we can match the accuracy of the traditional model, and more importantly, we can now make predictions about unseen rows with nearly the same accuracy as rows available at training time.
Adding Gradient Noise Improves Learning for Very Deep Networks
This success is partially attributed to architectural innovations such as convolutional and long short-term memory networks.
Neural Programmer: Inducing Latent Programs with Gradient Descent
In this work, we propose Neural Programmer, an end-to-end differentiable neural network augmented with a small set of basic arithmetic and logic operations.
Learning Dictionaries for Named Entity Recognition using Minimal Supervision
This paper describes an approach for automatic construction of dictionaries for Named Entity Recognition (NER) using large amounts of unlabeled data and a few seed examples.
Inferring Missing Entity Type Instances for Knowledge Base Completion: New Dataset and Methods
In this work, we focus on the task of inferring missing entity type instances in a KB, a fundamental task for KB competition yet receives little attention.
Compositional Vector Space Models for Knowledge Base Completion
Knowledge base (KB) completion adds new facts to a KB by making inferences from existing facts, for example by inferring with high likelihood nationality(X, Y) from bornIn(X, Y).
Efficient Non-parametric Estimation of Multiple Embeddings per Word in Vector Space
There is rising interest in vector-space word embeddings and their use in NLP, especially given recent methods for their fast estimation at very large scale.
