Search Results for author: Geoffrey Irving

Found 20 papers, 8 papers with code

Accelerating Large Language Model Decoding with Speculative Sampling

no code implementations2 Feb 2023 Charlie Chen, Sebastian Borgeaud, Geoffrey Irving, Jean-Baptiste Lespiau, Laurent SIfre, John Jumper

We present speculative sampling, an algorithm for accelerating transformer decoding by enabling the generation of multiple tokens from each transformer call.

Language Modelling Large Language Model

Solving math word problems with process- and outcome-based feedback

no code implementations25 Nov 2022 Jonathan Uesato, Nate Kushman, Ramana Kumar, Francis Song, Noah Siegel, Lisa Wang, Antonia Creswell, Geoffrey Irving, Irina Higgins

Recent work has shown that asking language models to generate reasoning steps improves performance on many reasoning tasks.

Ranked #10 on Arithmetic Reasoning on GSM8K (using extra training data)

Arithmetic Reasoning GSM8K

Fine-Tuning Language Models via Epistemic Neural Networks

1 code implementation3 Nov 2022 Ian Osband, Seyed Mohammad Asghari, Benjamin Van Roy, Nat McAleese, John Aslanides, Geoffrey Irving

Language models often pre-train on large unsupervised text corpora, then fine-tune on additional task-specific data.

Active Learning Language Modelling

Uncertainty Estimation for Language Reward Models

no code implementations14 Mar 2022 Adam Gleave, Geoffrey Irving

However, to solve a particular problem (such as text summarization) it is typically necessary to fine-tune them on a task-specific dataset.

Active Learning Reinforcement Learning (RL) +1

Red Teaming Language Models with Language Models

no code implementations7 Feb 2022 Ethan Perez, Saffron Huang, Francis Song, Trevor Cai, Roman Ring, John Aslanides, Amelia Glaese, Nat McAleese, Geoffrey Irving

In this work, we automatically find cases where a target LM behaves in a harmful way, by generating test cases ("red teaming") using another LM.

Chatbot Language Modelling +1

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

no code implementations NA 2021 Jack W. Rae, Sebastian Borgeaud, Trevor Cai, Katie Millican, Jordan Hoffmann, Francis Song, John Aslanides, Sarah Henderson, Roman Ring, Susannah Young, Eliza Rutherford, Tom Hennigan, Jacob Menick, Albin Cassirer, Richard Powell, George van den Driessche, Lisa Anne Hendricks, Maribeth Rauh, Po-Sen Huang, Amelia Glaese, Johannes Welbl, Sumanth Dathathri, Saffron Huang, Jonathan Uesato, John Mellor, Irina Higgins, Antonia Creswell, Nat McAleese, Amy Wu, Erich Elsen, Siddhant Jayakumar, Elena Buchatskaya, David Budden, Esme Sutherland, Karen Simonyan, Michela Paganini, Laurent SIfre, Lena Martens, Xiang Lorraine Li, Adhiguna Kuncoro, Aida Nematzadeh, Elena Gribovskaya, Domenic Donato, Angeliki Lazaridou, Arthur Mensch, Jean-Baptiste Lespiau, Maria Tsimpoukelli, Nikolai Grigorev, Doug Fritz, Thibault Sottiaux, Mantas Pajarskas, Toby Pohlen, Zhitao Gong, Daniel Toyama, Cyprien de Masson d'Autume, Yujia Li, Tayfun Terzi, Vladimir Mikulik, Igor Babuschkin, Aidan Clark, Diego de Las Casas, Aurelia Guy, Chris Jones, James Bradbury, Matthew Johnson, Blake Hechtman, Laura Weidinger, Iason Gabriel, William Isaac, Ed Lockhart, Simon Osindero, Laura Rimell, Chris Dyer, Oriol Vinyals, Kareem Ayoub, Jeff Stanway, Lorrayne Bennett, Demis Hassabis, Koray Kavukcuoglu, Geoffrey Irving

Language modelling provides a step towards intelligent communication systems by harnessing large repositories of written human knowledge to better predict and understand the world.

 Ranked #1 on College Mathematics on BIG-bench (using extra training data)

Abstract Algebra Anachronisms +133

Alignment of Language Agents

no code implementations26 Mar 2021 Zachary Kenton, Tom Everitt, Laura Weidinger, Iason Gabriel, Vladimir Mikulik, Geoffrey Irving

For artificial intelligence to be beneficial to humans the behaviour of AI agents needs to be aligned with what humans want.

Fine-Tuning Language Models from Human Preferences

6 code implementations18 Sep 2019 Daniel M. Ziegler, Nisan Stiennon, Jeffrey Wu, Tom B. Brown, Alec Radford, Dario Amodei, Paul Christiano, Geoffrey Irving

Most work on reward learning has used simulated environments, but complex information about values is often expressed in natural language, and we believe reward learning for language is a key to making RL practical and safe for real-world tasks.

Descriptive Language Modelling +1

AI safety via debate

1 code implementation2 May 2018 Geoffrey Irving, Paul Christiano, Dario Amodei

To make AI systems broadly useful for challenging real-world tasks, we need them to learn complex human goals and preferences.

Deep Network Guided Proof Search

no code implementations24 Jan 2017 Sarah Loos, Geoffrey Irving, Christian Szegedy, Cezary Kaliszyk

Here we suggest deep learning based guidance in the proof search of the theorem prover E. We train and compare several deep neural network models on the traces of existing ATP proofs of Mizar statements and use them to select processed clauses during proof search.

Game of Go Image Captioning +5

DeepMath - Deep Sequence Models for Premise Selection

2 code implementations NeurIPS 2016 Alex A. Alemi, Francois Chollet, Niklas Een, Geoffrey Irving, Christian Szegedy, Josef Urban

We study the effectiveness of neural sequence models for premise selection in automated theorem proving, one of the main bottlenecks in the formalization of mathematics.

Automated Theorem Proving

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