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Jordan Hoffmann

6 accepted papers

2022

A Systematic Investigation of Commonsense Knowledge in Large Language Models

EMNLP 2022main

Language models (LMs) trained on large amounts of data have shown impressive performance on many NLP tasks under the zero-shot and few-shot setup. Here we aim to better understand the extent to which such models learn commonsense knowledge — a critical component of many NLP applications. We conduct…

Cited by 72SourcePDFScholar
2022

An empirical analysis of compute-optimal large language model training

NeurIPS 2022accept

We investigate the optimal model size and number of tokens for training a transformer language model under a given compute budget. We find that current large language models are significantly undertrained, a consequence of the recent focus on scaling language models whilst keeping the amount of trai…

Cited by 171SourcePDFScholar
2022

Improving Language Models by Retrieving from Trillions of Tokens

ICML 2022spotlight

We enhance auto-regressive language models by conditioning on document chunks retrieved from a large corpus, based on local similarity with preceding tokens. With a 2 trillion token database, our Retrieval-Enhanced Transformer (RETRO) obtains comparable performance to GPT-3 and Jurassic-1 on the Pil…

2022

Unified Scaling Laws for Routed Language Models

ICML 2022oral

The performance of a language model has been shown to be effectively modeled as a power-law in its parameter count. Here we study the scaling behaviors of Routing Networks: architectures that conditionally use only a subset of their parameters while processing an input. For these models, parameter c…

2021

Recurrent Independent Mechanisms

ICLR 2021spotlight

We explore the hypothesis that learning modular structures which reflect the dynamics of the environment can lead to better generalization and robustness to changes that only affect a few of the underlying causes. We propose Recurrent Independent Mechanisms (RIMs), a new recurrent architecture in wh…

Cited by 389SourcePDFScholar
2019

vGraph: A Generative Model for Joint Community Detection and Node Representation Learning

NeurIPS 2019poster

This paper focuses on two fundamental tasks of graph analysis: community detection and node representation learning, which capture the global and local structures of graphs respectively. In existing literature, these two tasks are usually independently studied while they are actually highly correlat…