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08/23/2023
D4: Improving LLM Pretraining via Document De-Duplication and Diversification
Over recent years, an increasing amount of compute and data has been pou...
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05/12/2023
MEGABYTE: Predicting Million-byte Sequences with Multiscale Transformers
Autoregressive transformers are spectacular models for short sequences b...
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01/10/2023
Scaling Laws for Generative Mixed-Modal Language Models
Generative language models define distributions over sequences of tokens...
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11/29/2022
BARTSmiles: Generative Masked Language Models for Molecular Representations
We discover a robust self-supervised strategy tailored towards molecular...
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11/22/2022
Retrieval-Augmented Multimodal Language Modeling
Recent multimodal models such as DALL-E and CM3 have achieved remarkable...
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05/22/2022
Memorization Without Overfitting: Analyzing the Training Dynamics of Large Language Models
Despite their wide adoption, the underlying training and memorization dy...
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04/15/2022
Improving Passage Retrieval with Zero-Shot Question Generation
We propose a simple and effective re-ranking method for improving passag...
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04/12/2022
InCoder: A Generative Model for Code Infilling and Synthesis
Code is seldom written in a single left-to-right pass and is instead rep...
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01/19/2022
CM3: A Causal Masked Multimodal Model of the Internet
We introduce CM3, a family of causally masked generative models trained ...
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10/14/2021
CCQA: A New Web-Scale Question Answering Dataset for Model Pre-Training
With the rise of large-scale pre-trained language models, open-domain qu...
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09/28/2021
VideoCLIP: Contrastive Pre-training for Zero-shot Video-Text Understanding
We present VideoCLIP, a contrastive approach to pre-train a unified mode...
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07/14/2021
HTLM: Hyper-Text Pre-Training and Prompting of Language Models
We introduce HTLM, a hyper-text language model trained on a large-scale ...
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04/11/2021
Non-Autoregressive Semantic Parsing for Compositional Task-Oriented Dialog
Semantic parsing using sequence-to-sequence models allows parsing of dee...
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01/26/2021
Muppet: Massive Multi-task Representations with Pre-Finetuning
We propose pre-finetuning, an additional large-scale learning stage betw...
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12/22/2020
Intrinsic Dimensionality Explains the Effectiveness of Language Model Fine-Tuning
Although pretrained language models can be fine-tuned to produce state-o...
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09/28/2020
Conversational Semantic Parsing
The structured representation for semantic parsing in task-oriented assi...
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08/06/2020
Better Fine-Tuning by Reducing Representational Collapse
Although widely adopted, existing approaches for fine-tuning pre-trained...
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06/26/2020
Pre-training via Paraphrasing
We introduce MARGE, a pre-trained sequence-to-sequence model learned wit...
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09/23/2018
Towards Language Agnostic Universal Representations
When a bilingual student learns to solve word problems in math, we expec...
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02/21/2017
