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Found 13 papers, 5 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
Long-Context Understanding
on Ada-LEval (BestAnswer)
Generate, Annotate, and Learn: NLP with Synthetic Text
This paper studies the use of language models as a source of synthetic unlabeled text for NLP.
Multichannel Generative Language Model: Learning All Possible Factorizations Within and Across Channels
MGLM is a generative joint distribution model over channels.
Contextual Lensing of Universal Sentence Representations
We break the construction of universal sentence vectors into a core, variable length, sentence matrix representation equipped with an adaptable `lens' from which fixed-length vectors can be induced as a function of the lens context.
An Empirical Study of Generation Order for Machine Translation
In this work, we present an empirical study of generation order for machine translation.
Multichannel Generative Language Models
For conditional generation, the model is given a fully observed channel, and generates the k-1 channels in parallel.
Graph Normalizing Flows
We introduce graph normalizing flows: a new, reversible graph neural network model for prediction and generation.
ACTRCE: Augmenting Experience via Teacher’s Advice
Sparse reward is one of the most challenging problems in reinforcement learning (RL).
DOM-Q-NET: Grounded RL on Structured Language
Building agents to interact with the web would allow for significant improvements in knowledge understanding and representation learning.
ACTRCE: Augmenting Experience via Teacher's Advice For Multi-Goal Reinforcement Learning
We first analyze the differences among goal representation, and show that ACTRCE can efficiently solve difficult reinforcement learning problems in challenging 3D navigation tasks, whereas HER with non-language goal representation failed to learn.
Insertion Transformer: Flexible Sequence Generation via Insertion Operations
We present the Insertion Transformer, an iterative, partially autoregressive model for sequence generation based on insertion operations.
InferLite: Simple Universal Sentence Representations from Natural Language Inference Data
Natural language inference has been shown to be an effective supervised task for learning generic sentence embeddings.
Illustrative Language Understanding: Large-Scale Visual Grounding with Image Search
We introduce Picturebook, a large-scale lookup operation to ground language via {`}snapshots{'} of our physical world accessed through image search.
