Search Results for author: Arvind Neelakantan

Found 22 papers, 11 papers with code

GPT-4 Technical Report

9 code implementations Preprint 2023 OpenAI, :, Josh Achiam, Steven Adler, Sandhini Agarwal, Lama Ahmad, Ilge Akkaya, Florencia Leoni Aleman, Diogo Almeida, Janko Altenschmidt, Sam Altman, Shyamal Anadkat, Red Avila, Igor Babuschkin, Suchir Balaji, Valerie Balcom, Paul Baltescu, Haiming Bao, Mohammad Bavarian, Jeff Belgum, Irwan Bello, Jake Berdine, Gabriel Bernadett-Shapiro, Christopher Berner, Lenny Bogdonoff, Oleg Boiko, Madelaine Boyd, Anna-Luisa Brakman, Greg Brockman, Tim Brooks, Miles Brundage, Kevin Button, Trevor Cai, Rosie Campbell, Andrew Cann, Brittany Carey, Chelsea Carlson, Rory Carmichael, Brooke Chan, Che Chang, Fotis Chantzis, Derek Chen, Sully Chen, Ruby Chen, Jason Chen, Mark Chen, Ben Chess, Chester Cho, Casey Chu, Hyung Won Chung, Dave Cummings, Jeremiah Currier, Yunxing Dai, Cory Decareaux, Thomas Degry, Noah Deutsch, Damien Deville, Arka Dhar, David Dohan, Steve Dowling, Sheila Dunning, Adrien Ecoffet, Atty Eleti, Tyna Eloundou, David Farhi, Liam Fedus, Niko Felix, Simón Posada Fishman, Juston Forte, Isabella Fulford, Leo Gao, Elie Georges, Christian Gibson, Vik Goel, Tarun Gogineni, Gabriel Goh, Rapha Gontijo-Lopes, Jonathan Gordon, Morgan Grafstein, Scott Gray, Ryan Greene, Joshua Gross, Shixiang Shane Gu, Yufei Guo, Chris Hallacy, Jesse Han, Jeff Harris, Yuchen He, Mike Heaton, Johannes Heidecke, Chris Hesse, Alan Hickey, Wade Hickey, Peter Hoeschele, Brandon Houghton, Kenny Hsu, Shengli Hu, Xin Hu, Joost Huizinga, Shantanu Jain, Shawn Jain, Joanne Jang, Angela Jiang, Roger Jiang, Haozhun Jin, Denny Jin, Shino Jomoto, Billie Jonn, Heewoo Jun, Tomer Kaftan, Łukasz Kaiser, Ali Kamali, Ingmar Kanitscheider, Nitish Shirish Keskar, Tabarak Khan, Logan Kilpatrick, Jong Wook Kim, Christina Kim, Yongjik Kim, Jan Hendrik Kirchner, Jamie Kiros, Matt Knight, Daniel Kokotajlo, Łukasz Kondraciuk, Andrew Kondrich, Aris Konstantinidis, Kyle Kosic, Gretchen Krueger, Vishal Kuo, Michael Lampe, Ikai Lan, Teddy Lee, Jan Leike, Jade Leung, Daniel Levy, Chak Ming Li, Rachel Lim, Molly Lin, Stephanie Lin, Mateusz Litwin, Theresa Lopez, Ryan Lowe, Patricia Lue, Anna Makanju, Kim Malfacini, Sam Manning, Todor Markov, Yaniv Markovski, Bianca Martin, Katie Mayer, Andrew Mayne, Bob McGrew, Scott Mayer McKinney, Christine McLeavey, Paul McMillan, Jake McNeil, David Medina, Aalok Mehta, Jacob Menick, Luke Metz, Andrey Mishchenko, Pamela Mishkin, Vinnie Monaco, Evan Morikawa, Daniel Mossing, Tong Mu, Mira Murati, Oleg Murk, David Mély, Ashvin Nair, Reiichiro Nakano, Rajeev Nayak, Arvind Neelakantan, Richard Ngo, Hyeonwoo Noh, Long Ouyang, Cullen O'Keefe, Jakub Pachocki, Alex Paino, Joe Palermo, Ashley Pantuliano, Giambattista Parascandolo, Joel Parish, Emy Parparita, Alex Passos, Mikhail Pavlov, Andrew Peng, Adam Perelman, Filipe de Avila Belbute Peres, Michael Petrov, Henrique Ponde de Oliveira Pinto, Michael, Pokorny, Michelle Pokrass, Vitchyr H. Pong, Tolly Powell, Alethea Power, Boris Power, Elizabeth Proehl, Raul Puri, Alec Radford, Jack Rae, Aditya Ramesh, Cameron Raymond, Francis Real, Kendra Rimbach, Carl Ross, Bob Rotsted, Henri Roussez, Nick Ryder, Mario Saltarelli, Ted Sanders, Shibani Santurkar, Girish Sastry, Heather Schmidt, David Schnurr, John Schulman, Daniel Selsam, Kyla Sheppard, Toki Sherbakov, Jessica Shieh, Sarah Shoker, Pranav Shyam, Szymon Sidor, Eric Sigler, Maddie Simens, Jordan Sitkin, Katarina Slama, Ian Sohl, Benjamin Sokolowsky, Yang song, Natalie Staudacher, Felipe Petroski Such, Natalie Summers, Ilya Sutskever, Jie Tang, Nikolas Tezak, Madeleine B. Thompson, Phil Tillet, Amin Tootoonchian, Elizabeth Tseng, Preston Tuggle, Nick Turley, Jerry Tworek, Juan Felipe Cerón Uribe, Andrea Vallone, Arun Vijayvergiya, Chelsea Voss, Carroll Wainwright, Justin Jay Wang, Alvin Wang, Ben Wang, Jonathan Ward, Jason Wei, CJ Weinmann, Akila Welihinda, Peter Welinder, Jiayi Weng, Lilian Weng, Matt Wiethoff, Dave Willner, Clemens Winter, Samuel Wolrich, Hannah Wong, Lauren Workman, Sherwin Wu, Jeff Wu, Michael Wu, Kai Xiao, Tao Xu, Sarah Yoo, Kevin Yu, Qiming Yuan, Wojciech Zaremba, Rowan Zellers, Chong Zhang, Marvin Zhang, Shengjia Zhao, Tianhao Zheng, Juntang Zhuang, William Zhuk, Barret Zoph

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 Only Connect Walls Dataset Task 1 (Grouping) on OCW (using extra training data)

Arithmetic Reasoning Bug fixing +8

On Task-Level Dialogue Composition of Generative Transformer Model

1 code implementation EMNLP (insights) 2020 Prasanna Parthasarathi, Arvind Neelakantan, Sharan Narang

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.

Response Generation Task-Oriented Dialogue Systems

Trading Off Diversity and Quality in Natural Language Generation

no code implementations EACL (HumEval) 2021 Hugh Zhang, Daniel Duckworth, Daphne Ippolito, Arvind Neelakantan

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.

Text Generation

Neural Assistant: Joint Action Prediction, Response Generation, and Latent Knowledge Reasoning

1 code implementation31 Oct 2019 Arvind Neelakantan, Semih Yavuz, Sharan Narang, Vishaal Prasad, Ben Goodrich, Daniel Duckworth, Chinnadhurai Sankar, Xifeng Yan

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.

Response Generation Retrieval +1

Taskmaster-1: Toward a Realistic and Diverse Dialog Dataset

1 code implementation IJCNLP 2019 Bill Byrne, Karthik Krishnamoorthi, Chinnadhurai Sankar, Arvind Neelakantan, Daniel Duckworth, Semih Yavuz, Ben Goodrich, Amit Dubey, Andy Cedilnik, Kyu-Young Kim

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

no code implementations11 Jun 2019 Daniel Duckworth, Arvind Neelakantan, Ben Goodrich, Lukasz Kaiser, Samy Bengio

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.

Image Generation Response Generation +1

Towards a better understanding of Vector Quantized Autoencoders

no code implementations ICLR 2019 Aurko Roy, Ashish Vaswani, Niki Parmar, Arvind Neelakantan

Deep neural networks with discrete latent variables offer the promise of better symbolic reasoning, and learning abstractions that are more useful to new tasks.

Knowledge Distillation Machine Translation +1

Theory and Experiments on Vector Quantized Autoencoders

2 code implementations28 May 2018 Aurko Roy, Ashish Vaswani, Arvind Neelakantan, Niki Parmar

Deep neural networks with discrete latent variables offer the promise of better symbolic reasoning, and learning abstractions that are more useful to new tasks.

Image Generation Knowledge Distillation +2

Learning a Natural Language Interface with Neural Programmer

2 code implementations28 Nov 2016 Arvind Neelakantan, Quoc V. Le, Martin Abadi, Andrew McCallum, Dario Amodei

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.

Natural Language Queries Program induction +1

Chains of Reasoning over Entities, Relations, and Text using Recurrent Neural Networks

2 code implementations EACL 2017 Rajarshi Das, Arvind Neelakantan, David Belanger, Andrew McCallum

Our goal is to combine the rich multistep inference of symbolic logical reasoning with the generalization capabilities of neural networks.

Logical Reasoning

Generalizing to Unseen Entities and Entity Pairs with Row-less Universal Schema

1 code implementation EACL 2017 Patrick Verga, Arvind Neelakantan, Andrew McCallum

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.

Matrix Completion

Adding Gradient Noise Improves Learning for Very Deep Networks

4 code implementations21 Nov 2015 Arvind Neelakantan, Luke Vilnis, Quoc V. Le, Ilya Sutskever, Lukasz Kaiser, Karol Kurach, James Martens

This success is partially attributed to architectural innovations such as convolutional and long short-term memory networks.

Question Answering

Neural Programmer: Inducing Latent Programs with Gradient Descent

no code implementations16 Nov 2015 Arvind Neelakantan, Quoc V. Le, Ilya Sutskever

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.

Question Answering speech-recognition +1

Compositional Vector Space Models for Knowledge Base Completion

no code implementations IJCNLP 2015 Arvind Neelakantan, Benjamin Roth, Andrew McCallum

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).

Knowledge Base Completion Relation +1

Inferring Missing Entity Type Instances for Knowledge Base Completion: New Dataset and Methods

no code implementations HLT 2015 Arvind Neelakantan, Ming-Wei Chang

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.

Knowledge Base Completion Relation Extraction

Efficient Non-parametric Estimation of Multiple Embeddings per Word in Vector Space

no code implementations EMNLP 2014 Arvind Neelakantan, Jeevan Shankar, Alexandre Passos, Andrew McCallum

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.

Vocal Bursts Type Prediction Word Embeddings +1

Learning Dictionaries for Named Entity Recognition using Minimal Supervision

no code implementations EACL 2014 Arvind Neelakantan, Michael Collins

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.

named-entity-recognition Named Entity Recognition +2

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