‘T5 Transformer’ tag

Gwern Branwen

See Also
Links
Miscellaneous
Link Bibliography

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Links

“Chronos: Learning the Language of Time Series”, Ansari et al 2024

Chronos: Learning the Language of Time Series

“ELLA: Equip Diffusion Models With LLM for Enhanced Semantic Alignment”, Hu et al 2024

ELLA: Equip Diffusion Models with LLM for Enhanced Semantic Alignment

“How to Train Data-Efficient LLMs”, Sachdeva et al 2024

How to Train Data-Efficient LLMs

“Time Vectors: Time Is Encoded in the Weights of Finetuned Language Models”, Nylund et al 2023

Time Vectors: Time is Encoded in the Weights of Finetuned Language Models

“Rich Human Feedback for Text-To-Image Generation”, Liang et al 2023

Rich Human Feedback for Text-to-Image Generation

“Helping or Herding? Reward Model Ensembles Mitigate but Do Not Eliminate Reward Hacking”, Eisenstein et al 2023

Helping or Herding? Reward Model Ensembles Mitigate but do not Eliminate Reward Hacking

“Instruction-Tuning Aligns LLMs to the Human Brain”, Aw et al 2023

Instruction-tuning Aligns LLMs to the Human Brain

“PEARL: Personalizing Large Language Model Writing Assistants With Generation-Calibrated Retrievers”, Mysore et al 2023

PEARL: Personalizing Large Language Model Writing Assistants with Generation-Calibrated Retrievers

“UT5: Pretraining Non Autoregressive T5 With Unrolled Denoising”, Salem et al 2023

UT5: Pretraining Non autoregressive T5 with unrolled denoising

“FreshLLMs: Refreshing Large Language Models With Search Engine Augmentation”, Vu et al 2023

FreshLLMs: Refreshing Large Language Models with Search Engine Augmentation

“MADLAD-400: A Multilingual And Document-Level Large Audited Dataset”, Kudugunta et al 2023

MADLAD-400: A Multilingual And Document-Level Large Audited Dataset

“DialogStudio: Towards Richest and Most Diverse Unified Dataset Collection for Conversational AI”, Zhang et al 2023

DialogStudio: Towards Richest and Most Diverse Unified Dataset Collection for Conversational AI

“No Train No Gain: Revisiting Efficient Training Algorithms For Transformer-Based Language Models”, Kaddour et al 2023

No Train No Gain: Revisiting Efficient Training Algorithms For Transformer-based Language Models

“GKD: Generalized Knowledge Distillation for Auto-Regressive Sequence Models”, Agarwal et al 2023

GKD: Generalized Knowledge Distillation for Auto-regressive Sequence Models

“PaLI-X: On Scaling up a Multilingual Vision and Language Model”, Chen et al 2023

PaLI-X: On Scaling up a Multilingual Vision and Language Model

“Learning to Generate Novel Scientific Directions With Contextualized Literature-Based Discovery”, Wang et al 2023

Learning to Generate Novel Scientific Directions with Contextualized Literature-based Discovery

“SoundStorm: Efficient Parallel Audio Generation”, Borsos et al 2023

SoundStorm: Efficient Parallel Audio Generation

“Distilling Step-By-Step! Outperforming Larger Language Models With Less Training Data and Smaller Model Sizes”, Hsieh et al 2023

Distilling Step-by-Step! Outperforming Larger Language Models with Less Training Data and Smaller Model Sizes

“LaMini-LM: A Diverse Herd of Distilled Models from Large-Scale Instructions”, Wu et al 2023

LaMini-LM: A Diverse Herd of Distilled Models from Large-Scale Instructions

“TANGO: Text-To-Audio Generation Using Instruction-Tuned LLM and Latent Diffusion Model”, Ghosal et al 2023

TANGO: Text-to-Audio Generation using Instruction-Tuned LLM and Latent Diffusion Model

“Learning to Compress Prompts With Gist Tokens”, Mu et al 2023

Learning to Compress Prompts with Gist Tokens

“BiLD: Big Little Transformer Decoder”, Kim et al 2023

BiLD: Big Little Transformer Decoder

“Speak, Read and Prompt (SPEAR-TTS): High-Fidelity Text-To-Speech With Minimal Supervision”, Kharitonov et al 2023

Speak, Read and Prompt (SPEAR-TTS): High-Fidelity Text-to-Speech with Minimal Supervision

“BLIP-2: Bootstrapping Language-Image Pre-Training With Frozen Image Encoders and Large Language Models”, Li et al 2023

BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Models

“InPars-Light: Cost-Effective Unsupervised Training of Efficient Rankers”, Boytsov et al 2023

InPars-Light: Cost-Effective Unsupervised Training of Efficient Rankers

“Muse: Text-To-Image Generation via Masked Generative Transformers”, Chang et al 2023

Muse: Text-To-Image Generation via Masked Generative Transformers

“Character-Aware Models Improve Visual Text Rendering”, Liu et al 2022

Character-Aware Models Improve Visual Text Rendering

“Unnatural Instructions: Tuning Language Models With (Almost) No Human Labor”, Honovich et al 2022

Unnatural Instructions: Tuning Language Models with (Almost) No Human Labor

“One Embedder, Any Task: Instruction-Finetuned Text Embeddings (INSTRUCTOR)”, Su et al 2022

One Embedder, Any Task: Instruction-Finetuned Text Embeddings (INSTRUCTOR)

“ERNIE-Code: Beyond English-Centric Cross-Lingual Pretraining for Programming Languages”, Chai et al 2022

ERNIE-Code: Beyond English-Centric Cross-lingual Pretraining for Programming Languages

“Sparse Upcycling: Training Mixture-Of-Experts from Dense Checkpoints”, Komatsuzaki et al 2022

Sparse Upcycling: Training Mixture-of-Experts from Dense Checkpoints

“Fast Inference from Transformers via Speculative Decoding”, Leviathan et al 2022

Fast Inference from Transformers via Speculative Decoding

“I Can’t Believe There’s No Images! Learning Visual Tasks Using Only Language Data”, Gu et al 2022

I Can’t Believe There’s No Images! Learning Visual Tasks Using only Language Data

“TART: Task-Aware Retrieval With Instructions”, Asai et al 2022

TART: Task-aware Retrieval with Instructions

“BLOOMZ/mT0: Crosslingual Generalization through Multitask Finetuning”, Muennighoff et al 2022

BLOOMZ/mT0: Crosslingual Generalization through Multitask Finetuning

“EDiff-I: Text-To-Image Diffusion Models With an Ensemble of Expert Denoisers”, Balaji et al 2022

eDiff-I: Text-to-Image Diffusion Models with an Ensemble of Expert Denoisers

“ProMoT: Preserving In-Context Learning Ability in Large Language Model Fine-Tuning”, Wang et al 2022

ProMoT: Preserving In-Context Learning ability in Large Language Model Fine-tuning

“Help Me Write a Poem: Instruction Tuning As a Vehicle for Collaborative Poetry Writing (CoPoet)”, Chakrabarty et al 2022

Help me write a poem: Instruction Tuning as a Vehicle for Collaborative Poetry Writing (CoPoet)

“FLAN: Scaling Instruction-Finetuned Language Models”, Chung et al 2022

FLAN: Scaling Instruction-Finetuned Language Models

“Table-To-Text Generation and Pre-Training With TabT5”, Andrejczuk et al 2022

Table-To-Text generation and pre-training with TabT5

“GLM-130B: An Open Bilingual Pre-Trained Model”, Zeng et al 2022

GLM-130B: An Open Bilingual Pre-trained Model

“SAP: Bidirectional Language Models Are Also Few-Shot Learners”, Patel et al 2022

SAP: Bidirectional Language Models Are Also Few-shot Learners

“FiD-Light: Efficient and Effective Retrieval-Augmented Text Generation”, Hofstätter et al 2022

FiD-Light: Efficient and Effective Retrieval-Augmented Text Generation

“PaLI: A Jointly-Scaled Multilingual Language-Image Model”, Chen et al 2022

PaLI: A Jointly-Scaled Multilingual Language-Image Model

“Training a T5 Using Lab-Sized Resources”, Ciosici & Derczynski 2022

Training a T5 Using Lab-sized Resources

“PEER: A Collaborative Language Model”, Schick et al 2022

PEER: A Collaborative Language Model

“Z-Code++: A Pre-Trained Language Model Optimized for Abstractive Summarization”, He et al 2022

Z-Code++: A Pre-trained Language Model Optimized for Abstractive Summarization

“Limitations of Language Models in Arithmetic and Symbolic Induction”, Qian et al 2022

Limitations of Language Models in Arithmetic and Symbolic Induction

“RealTime QA: What’s the Answer Right Now?”, Kasai et al 2022

RealTime QA: What’s the Answer Right Now?

“Forecasting Future World Events With Neural Networks”, Zou et al 2022

Forecasting Future World Events with Neural Networks

“RST: ReStructured Pre-Training”, Yuan & Liu 2022

RST: reStructured Pre-training

“Alexa Teacher Model: Pretraining and Distilling Multi-Billion-Parameter Encoders for Natural Language Understanding Systems”, FitzGerald et al 2022

Alexa Teacher Model: Pretraining and Distilling Multi-Billion-Parameter Encoders for Natural Language Understanding Systems

“Boosting Search Engines With Interactive Agents”, Ciaramita et al 2022

Boosting Search Engines with Interactive Agents

“EdiT5: Semi-Autoregressive Text-Editing With T5 Warm-Start”, Mallinson et al 2022

EdiT5: Semi-Autoregressive Text-Editing with T5 Warm-Start

“CT0: Fine-Tuned Language Models Are Continual Learners”, Scialom et al 2022

CT0: Fine-tuned Language Models are Continual Learners

“Imagen: Photorealistic Text-To-Image Diffusion Models With Deep Language Understanding”, Saharia et al 2022

Imagen: Photorealistic Text-to-Image Diffusion Models with Deep Language Understanding

“Automated Crossword Solving”, Wallace et al 2022

Automated Crossword Solving

“Unifying Language Learning Paradigms”, Tay et al 2022

Unifying Language Learning Paradigms

“Tk-Instruct: Benchmarking Generalization via In-Context Instructions on 1,600+ Language Tasks”, Wang et al 2022

Tk-Instruct: Benchmarking Generalization via In-Context Instructions on 1,600+ Language Tasks

“What Language Model Architecture and Pretraining Objective Work Best for Zero-Shot Generalization?”, Wang et al 2022

What Language Model Architecture and Pretraining Objective Work Best for Zero-Shot Generalization?

“ByT5 Model for Massively Multilingual Grapheme-To-Phoneme Conversion”, Zhu et al 2022

ByT5 model for massively multilingual grapheme-to-phoneme conversion

“Pathways: Asynchronous Distributed Dataflow for ML”, Barham et al 2022

Pathways: Asynchronous Distributed Dataflow for ML

“HyperPrompt: Prompt-Based Task-Conditioning of Transformers”, He et al 2022

HyperPrompt: Prompt-based Task-Conditioning of Transformers

“Using Natural Language Prompts for Machine Translation”, Garcia & Firat 2022

Using natural language prompts for machine translation

“UnifiedQA-V2: Stronger Generalization via Broader Cross-Format Training”, Khashabi et al 2022

UnifiedQA-v2: Stronger Generalization via Broader Cross-Format Training

“Mixture-Of-Experts With Expert Choice Routing”, Zhou et al 2022

Mixture-of-Experts with Expert Choice Routing

“InPars: Data Augmentation for Information Retrieval Using Large Language Models”, Bonifacio et al 2022

InPars: Data Augmentation for Information Retrieval using Large Language Models

“Reasoning Like Program Executors”, Pi et al 2022

Reasoning Like Program Executors

“CommonsenseQA 2.0: Exposing the Limits of AI through Gamification”, Talmor et al 2022

CommonsenseQA 2.0: Exposing the Limits of AI through Gamification

“FRUIT: Faithfully Reflecting Updated Information in Text”, IV et al 2021

FRUIT: Faithfully Reflecting Updated Information in Text

“Large Dual Encoders Are Generalizable Retrievers”, Ni et al 2021

Large Dual Encoders Are Generalizable Retrievers

“LongT5: Efficient Text-To-Text Transformer for Long Sequences”, Guo et al 2021

LongT5: Efficient Text-To-Text Transformer for Long Sequences

“Scaling Language Models: Methods, Analysis & Insights from Training Gopher”, Rae et al 2021

Scaling Language Models: Methods, Analysis & Insights from Training Gopher

“ExT5: Towards Extreme Multi-Task Scaling for Transfer Learning”, Aribandi et al 2021

ExT5: Towards Extreme Multi-Task Scaling for Transfer Learning

“Fast Model Editing at Scale”, Mitchell et al 2021

Fast Model Editing at Scale

“T0: Multitask Prompted Training Enables Zero-Shot Task Generalization”, Sanh et al 2021

T0: Multitask Prompted Training Enables Zero-Shot Task Generalization

“LFPT5: A Unified Framework for Lifelong Few-Shot Language Learning Based on Prompt Tuning of T5”, Qin & Joty 2021

LFPT5: A Unified Framework for Lifelong Few-shot Language Learning Based on Prompt Tuning of T5

“Can Machines Learn Morality? The Delphi Experiment”, Jiang et al 2021

Can Machines Learn Morality? The Delphi Experiment

“Scale Efficiently: Insights from Pre-Training and Fine-Tuning Transformers”, Tay et al 2021

Scale Efficiently: Insights from Pre-training and Fine-tuning Transformers

“TruthfulQA: Measuring How Models Mimic Human Falsehoods”, Lin et al 2021

TruthfulQA: Measuring How Models Mimic Human Falsehoods

“General-Purpose Question-Answering With Macaw”, Tafjord & Clark 2021

General-Purpose Question-Answering with Macaw

“Sentence-T5: Scalable Sentence Encoders from Pre-Trained Text-To-Text Models”, Ni et al 2021

Sentence-T5: Scalable Sentence Encoders from Pre-trained Text-to-Text Models

“Time-Aware Language Models As Temporal Knowledge Bases”, Dhingra et al 2021

Time-Aware Language Models as Temporal Knowledge Bases

“Implicit Representations of Meaning in Neural Language Models”, Li et al 2021

Implicit Representations of Meaning in Neural Language Models

“Explainable Multi-Hop Verbal Reasoning Through Internal Monologue”, Liang et al 2021

Explainable Multi-hop Verbal Reasoning Through Internal Monologue

“ByT5: Towards a Token-Free Future With Pre-Trained Byte-To-Byte Models”, Xue et al 2021

ByT5: Towards a token-free future with pre-trained byte-to-byte models

“Carbon Emissions and Large Neural Network Training”, Patterson et al 2021

Carbon Emissions and Large Neural Network Training

“The Power of Scale for Parameter-Efficient Prompt Tuning”, Lester et al 2021

The Power of Scale for Parameter-Efficient Prompt Tuning

“UNICORN on RAINBOW: A Universal Commonsense Reasoning Model on a New Multitask Benchmark”, Lourie et al 2021

UNICORN on RAINBOW: A Universal Commonsense Reasoning Model on a New Multitask Benchmark

“GLM: General Language Model Pretraining With Autoregressive Blank Infilling”, Du et al 2021

GLM: General Language Model Pretraining with Autoregressive Blank Infilling

“Investigating the Limitations of the Transformers With Simple Arithmetic Tasks”, Nogueira et al 2021

Investigating the Limitations of the Transformers with Simple Arithmetic Tasks

“VL-T5: Unifying Vision-And-Language Tasks via Text Generation”, Cho et al 2021

VL-T5: Unifying Vision-and-Language Tasks via Text Generation

“Switch Transformers: Scaling to Trillion Parameter Models With Simple and Efficient Sparsity”, Fedus et al 2021

Switch Transformers: Scaling to Trillion Parameter Models with Simple and Efficient Sparsity

“MT5: A Massively Multilingual Pre-Trained Text-To-Text Transformer”, Xue et al 2020

mT5: A massively multilingual pre-trained text-to-text transformer

“TextSETTR: Few-Shot Text Style Extraction and Tunable Targeted Restyling”, Riley et al 2020

TextSETTR: Few-Shot Text Style Extraction and Tunable Targeted Restyling

“MMLU: Measuring Massive Multitask Language Understanding”, Hendrycks et al 2020

MMLU: Measuring Massive Multitask Language Understanding

“ProtTrans: Towards Cracking the Language of Life’s Code Through Self-Supervised Deep Learning and High Performance Computing”, Elnaggar et al 2020

ProtTrans: Towards Cracking the Language of Life’s Code Through Self-Supervised Deep Learning and High Performance Computing

“Leveraging Passage Retrieval With Generative Models for Open Domain Question Answering”, Izacard & Grave 2020

Leveraging Passage Retrieval with Generative Models for Open Domain Question Answering

“UnifiedQA: Crossing Format Boundaries With a Single QA System”, Khashabi et al 2020

UnifiedQA: Crossing Format Boundaries With a Single QA System

“TTTTTackling WinoGrande Schemas”, Lin et al 2020

TTTTTackling WinoGrande Schemas

“How Much Knowledge Can You Pack Into the Parameters of a Language Model?”, Roberts et al 2020

How Much Knowledge Can You Pack Into the Parameters of a Language Model?

“CommonGen: A Constrained Text Generation Challenge for Generative Commonsense Reasoning”, Lin et al 2019

CommonGen: A Constrained Text Generation Challenge for Generative Commonsense Reasoning

“T5: Exploring the Limits of Transfer Learning With a Unified Text-To-Text Transformer”, Raffel et al 2019

T5: Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer

“Transformer-VAE for Program Synthesis”

Transformer-VAE for Program Synthesis

colinraffel

I recently came across https://arxiv.org/abs/2004.08900, which 'assumes 2-3 runs' of T5-11B. In fact, we trained T5-11B once. That's why we spend 35 pages figuring out how we should train before we start training. You don't want to mess up a training run that big.⁠:

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Miscellaneous

Link Bibliography

https://arxiv.org/abs/2310.03214#google: “FreshLLMs: Refreshing Large Language Models With Search Engine Augmentation”, Tu Vu, Mohit Iyyer, Xuezhi Wang, Noah Constant, Jerry Wei, Jason Wei, Chris Tar, Yun-Hsuan Sung, Denny Zhou, Quoc Le, Thang Luong

link-bibliography
https://arxiv.org/abs/2307.06440: “No Train No Gain: Revisiting Efficient Training Algorithms For Transformer-Based Language Models”, Jean Kaddour, Oscar Key, Piotr Nawrot, Pasquale Minervini, Matt J. Kusner

link-bibliography
https://arxiv.org/abs/2305.09636#google: “SoundStorm: Efficient Parallel Audio Generation”, Zalán Borsos, Matt Sharifi, Damien Vincent, Eugene Kharitonov, Neil Zeghidour, Marco Tagliasacchi

link-bibliography
https://arxiv.org/abs/2305.02301#google: “Distilling Step-By-Step! Outperforming Larger Language Models With Less Training Data and Smaller Model Sizes”, Cheng-Yu Hsieh, Chun-Liang Li, Chih-Kuan Yeh, Hootan Nakhost, Yasuhisa Fujii, Alexander Ratner, Ranjay Krishna, Chen-Yu Lee, Tomas Pfister

link-bibliography
https://arxiv.org/abs/2304.13731: “TANGO: Text-To-Audio Generation Using Instruction-Tuned LLM and Latent Diffusion Model”, Deepanway Ghosal, Navonil Majumder, Ambuj Mehrish, Soujanya Poria

link-bibliography
https://arxiv.org/abs/2304.08467: “Learning to Compress Prompts With Gist Tokens”, Jesse Mu, Xiang Lisa Li, Noah Goodman

link-bibliography
https://arxiv.org/abs/2301.12597#salesforce: “BLIP-2: Bootstrapping Language-Image Pre-Training With Frozen Image Encoders and Large Language Models”, Junnan Li, Dongxu Li, Silvio Savarese, Steven Hoi

link-bibliography
https://arxiv.org/abs/2301.00704#google: “Muse: Text-To-Image Generation via Masked Generative Transformers”, Huiwen Chang, Han Zhang, Jarred Barber, A. J. Maschinot, Jose Lezama, Lu Jiang, Ming-Hsuan Yang, Kevin Murphy, William T. Freeman, Michael Rubinstein, Yuanzhen Li, Dilip Krishnan

link-bibliography
https://arxiv.org/abs/2212.10562#google: “Character-Aware Models Improve Visual Text Rendering”, Rosanne Liu, Dan Garrette, Chitwan Saharia, William Chan, Adam Roberts, Sharan Narang, Irina Blok, R. J. Mical, Mohammad Norouzi, Noah Constant

link-bibliography
https://arxiv.org/abs/2212.09741: “One Embedder, Any Task: Instruction-Finetuned Text Embeddings (INSTRUCTOR)”, Hongjin Su, Weijia Shi, Jungo Kasai, Yizhong Wang, Yushi Hu, Mari Ostendorf, Wen-tau Yih, Noah Smith, Luke Zettlemoyer, Tao Yu

link-bibliography
https://arxiv.org/abs/2212.05055#google: “Sparse Upcycling: Training Mixture-Of-Experts from Dense Checkpoints”, Aran Komatsuzaki, Joan Puigcerver, James Lee-Thorp, Carlos Riquelme Ruiz, Basil Mustafa, Joshua Ainslie, Yi Tay, Mostafa Dehghani, Neil Houlsby

link-bibliography
https://arxiv.org/abs/2211.01786: “BLOOMZ/mT0: Crosslingual Generalization through Multitask Finetuning”, Niklas Muennighoff, Thomas Wang, Lintang Sutawika, Adam Roberts, Stella Biderman, Teven Le Scao, M. Saiful Bari, Sheng Shen, Zheng-Xin Yong, Hailey Schoelkopf, Xiangru Tang, Dragomir Radev, Alham Fikri Aji, Khalid Almubarak, Samuel Albanie, Zaid Alyafeai, Albert Webson, Edward Raff, Colin Raffel

link-bibliography
https://arxiv.org/abs/2211.01324#nvidia: “EDiff-I: Text-To-Image Diffusion Models With an Ensemble of Expert Denoisers”, Yogesh Balaji, Seungjun Nah, Xun Huang, Arash Vahdat, Jiaming Song, Karsten Kreis, Miika Aittala, Timo Aila, Samuli Laine, Bryan Catanzaro, Tero Karras, Ming-Yu Liu

link-bibliography
https://arxiv.org/abs/2210.13669: “Help Me Write a Poem: Instruction Tuning As a Vehicle for Collaborative Poetry Writing (CoPoet)”, Tuhin Chakrabarty, Vishakh Padmakumar, He He

link-bibliography
https://arxiv.org/abs/2210.11416#google: “FLAN: Scaling Instruction-Finetuned Language Models”, Hyung Won Chung, Le Hou, Shayne Longpre, Barret Zoph, Yi Tay, William Fedus, Yunxuan Li, Xuezhi Wang, Mostafa Dehghani, Siddhartha Brahma, Albert Webson, Shixiang Shane Gu, Zhuyun Dai, Mirac Suzgun, Xinyun Chen, Aakanksha Chowdhery, Alex Castro-Ros, Marie Pellat, Kevin Robinson, Dasha Valter, Sharan Narang, Gaurav Mishra, Adams Yu, Vincent Zhao, Yanping Huang, Andrew Dai, Hongkun Yu, Slav Petrov, Ed H. Chi, Jeff Dean, Jacob Devlin, Adam Roberts, Denny Zhou, Quoc V. Le, Jason Wei

link-bibliography
https://arxiv.org/abs/2210.02414#baai: “GLM-130B: An Open Bilingual Pre-Trained Model”, Aohan Zeng, Xiao Liu, Zhengxiao Du, Zihan Wang, Hanyu Lai, Ming Ding, Zhuoyi Yang, Yifan Xu, Wendi Zheng, Xiao Xia, Weng Lam Tam, Zixuan Ma, Yufei Xue, Jidong Zhai, Wenguang Chen, Peng Zhang, Yuxiao Dong, Jie Tang

link-bibliography
https://arxiv.org/abs/2209.14500: “SAP: Bidirectional Language Models Are Also Few-Shot Learners”, Ajay Patel, Bryan Li, Mohammad Sadegh Rasooli, Noah Constant, Colin Raffel, Chris Callison-Burch

link-bibliography
https://arxiv.org/abs/2208.11663#facebook: “PEER: A Collaborative Language Model”, Timo Schick, Jane Dwivedi-Yu, Zhengbao Jiang, Fabio Petroni, Patrick Lewis, Gautier Izacard, Qingfei You, Christoforos Nalmpantis, Edouard Grave, Sebastian Riedel

link-bibliography
https://arxiv.org/abs/2208.09770#microsoft: “Z-Code++: A Pre-Trained Language Model Optimized for Abstractive Summarization”, Pengcheng He, Baolin Peng, Liyang Lu, Song Wang, Jie Mei, Yang Liu, Ruochen Xu, Hany Hassan Awadalla, Yu Shi, Chenguang Zhu, Wayne Xiong, Michael Zeng, Jianfeng Gao, Xuedong Huang

link-bibliography
https://arxiv.org/abs/2206.15474: “Forecasting Future World Events With Neural Networks”, Andy Zou, Tristan Xiao, Ryan Jia, Joe Kwon, Mantas Mazeika, Richard Li, Dawn Song, Jacob Steinhardt, Owain Evans, Dan Hendrycks

link-bibliography
https://arxiv.org/abs/2206.07808#amazon: “Alexa Teacher Model: Pretraining and Distilling Multi-Billion-Parameter Encoders for Natural Language Understanding Systems”, Jack FitzGerald, Shankar Ananthakrishnan, Konstantine Arkoudas, Davide Bernardi, Abhishek Bhagia, Claudio Delli Bovi, Jin Cao, Rakesh Chada, Amit Chauhan, Luoxin Chen, Anurag Dwarakanath, Satyam Dwivedi, Turan Gojayev, Karthik Gopalakrishnan, Thomas Gueudre, Dilek Hakkani-Tur, Wael Hamza, Jonathan Hueser, Kevin Martin Jose, Haidar Khan, Beiye Liu, Jianhua Lu, Alessandro Manzotti, Pradeep Natarajan, Karolina Owczarzak, Gokmen Oz, Enrico Palumbo, Charith Peris, Chandana Satya Prakash, Stephen Rawls, Andy Rosenbaum, Anjali Shenoy, Saleh Soltan, Mukund Harakere Sridhar, Liz Tan, Fabian Triefenbach, Pan Wei, Haiyang Yu, Shuai Zheng, Gokhan Tur, Prem Natarajan

link-bibliography
https://openreview.net/forum?id=0ZbPmmB61g#google: “Boosting Search Engines With Interactive Agents”, Massimiliano Ciaramita, Leonard Adolphs, Michelle Chen Huebscher, Sascha Rothe, Christian Buck, Thomas Hofmann, Yannic Kilcher, Lasse Espeholt, Pier Giuseppe Sessa, Lierni Sestorain, Benjamin Börschinger

link-bibliography
https://arxiv.org/abs/2205.12209#google: “EdiT5: Semi-Autoregressive Text-Editing With T5 Warm-Start”, Jonathan Mallinson, Jakub Adamek, Eric Malmi, Aliaksei Severyn

link-bibliography
https://arxiv.org/abs/2205.12393: “CT0: Fine-Tuned Language Models Are Continual Learners”, Thomas Scialom, Tuhin Chakrabarty, Smaranda Muresan

link-bibliography
https://arxiv.org/abs/2205.11487#google: “Imagen: Photorealistic Text-To-Image Diffusion Models With Deep Language Understanding”, Chitwan Saharia, William Chan, Saurabh Saxena, Lala Li, Jay Whang, Emily Denton, Seyed Kamyar Seyed Ghasemipour, Burcu Karagol Ayan, S. Sara Mahdavi, Rapha Gontijo Lopes, Tim Salimans, Jonathan Ho, David J. Fleet, Mohammad Norouzi

link-bibliography
https://arxiv.org/abs/2205.09665#bair: “Automated Crossword Solving”, Eric Wallace, Nicholas Tomlin, Albert Xu, Kevin Yang, Eshaan Pathak, Matthew Ginsberg, Dan Klein

link-bibliography
https://arxiv.org/abs/2205.05131#google: “Unifying Language Learning Paradigms”, Yi Tay, Mostafa Dehghani, Vinh Q. Tran, Xavier Garcia, Dara Bahri, Tal Schuster, Huaixiu Steven Zheng, Neil Houlsby, Donald Metzler

link-bibliography
https://arxiv.org/abs/2204.07705: “Tk-Instruct: Benchmarking Generalization via In-Context Instructions on 1,600+ Language Tasks”, Yizhong Wang, Swaroop Mishra, Pegah Alipoormolabashi, Yeganeh Kordi, Amirreza Mirzaei, Anjana Arunkumar, Arjun Ashok, Arut Selvan Dhanasekaran, Atharva Naik, David Stap, Eshaan Pathak, Giannis Karamanolakis, Haizhi Gary Lai, Ishan Purohit, Ishani Mondal, Jacob Anderson, Kirby Kuznia, Krima Doshi, Maitreya Patel, Kuntal Kumar Pal, Mehrad Moradshahi, Mihir Parmar, Mirali Purohit, Neeraj Varshney, Phani Rohitha Kaza, Pulkit Verma, Ravsehaj Singh Puri, Rushang Karia, Shailaja Keyur Sampat, Savan Doshi, Siddhartha Mishra, Sujan Reddy, Sumanta Patro, Tanay Dixit, Xudong Shen, Chitta Baral, Yejin Choi, <a href=https://nasmith.github.io/>N. A. Smith</a>, Hannaneh Hajishirzi, Daniel Khashabi

link-bibliography
https://arxiv.org/abs/2204.03067: “ByT5 Model for Massively Multilingual Grapheme-To-Phoneme Conversion”, Jian Zhu, Cong Zhang, David Jurgens

link-bibliography
https://arxiv.org/abs/2203.00759: “HyperPrompt: Prompt-Based Task-Conditioning of Transformers”, Yun He, Huaixiu Steven Zheng, Yi Tay, Jai Gupta, Yu Du, Vamsi Aribandi, Zhe Zhao, YaGuang Li, Zhao Chen, Donald Metzler, Heng-Tze Cheng, Ed H. Chi

link-bibliography
https://arxiv.org/abs/2202.11822#google: “Using Natural Language Prompts for Machine Translation”, Xavier Garcia, Orhan Firat

link-bibliography
https://arxiv.org/abs/2202.09368#google: “Mixture-Of-Experts With Expert Choice Routing”, Yanqi Zhou, Tao Lei, Hanxiao Liu, Nan Du, Yanping Huang, Vincent Zhao, Andrew Dai, Zhifeng Chen, Quoc Le, James Laudon

link-bibliography
https://arxiv.org/abs/2201.11473#microsoft: “Reasoning Like Program Executors”, Xinyu Pi, Qian Liu, Bei Chen, Morteza Ziyadi, Zeqi Lin, Yan Gao, Qiang Fu, Jian-Guang Lou, Weizhu Chen

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https://arxiv.org/abs/2108.08877#google: “Sentence-T5: Scalable Sentence Encoders from Pre-Trained Text-To-Text Models”, Jianmo Ni, Gustavo Hernández Ábrego, Noah Constant, Ji Ma, Keith B. Hall, Daniel Cer, Yinfei Yang

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https://arxiv.org/abs/2105.13626#google: “ByT5: Towards a Token-Free Future With Pre-Trained Byte-To-Byte Models”, Linting Xue, Aditya Barua, Noah Constant, Rami Al-Rfou, Sharan Narang, Mihir Kale, Adam Roberts, Colin Raffel

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https://arxiv.org/abs/2104.10350#google: “Carbon Emissions and Large Neural Network Training”, David Patterson, Joseph Gonzalez, Quoc Le, Chen Liang, Lluis-Miquel Munguia, Daniel Rothchild, David So, Maud Texier, Jeff Dean

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https://arxiv.org/abs/2101.03961#google: “Switch Transformers: Scaling to Trillion Parameter Models With Simple and Efficient Sparsity”, William Fedus, Barret Zoph, Noam Shazeer

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