Search Results for author: Maria Lomeli

Found 12 papers, 5 papers with code

In-Context Pretraining: Language Modeling Beyond Document Boundaries

no code implementations16 Oct 2023 Weijia Shi, Sewon Min, Maria Lomeli, Chunting Zhou, Margaret Li, Xi Victoria Lin, Noah A. Smith, Luke Zettlemoyer, Scott Yih, Mike Lewis

Large language models (LMs) are currently trained to predict tokens given document prefixes, enabling them to directly perform long-form generation and prompting-style tasks which can be reduced to document completion.

Language Modelling Reading Comprehension

RA-DIT: Retrieval-Augmented Dual Instruction Tuning

no code implementations2 Oct 2023 Xi Victoria Lin, Xilun Chen, Mingda Chen, Weijia Shi, Maria Lomeli, Rich James, Pedro Rodriguez, Jacob Kahn, Gergely Szilvasy, Mike Lewis, Luke Zettlemoyer, Scott Yih

Retrieval-augmented language models (RALMs) improve performance by accessing long-tail and up-to-date knowledge from external data stores, but are challenging to build.

Few-Shot Learning Open-Domain Question Answering +1

EditEval: An Instruction-Based Benchmark for Text Improvements

1 code implementation27 Sep 2022 Jane Dwivedi-Yu, Timo Schick, Zhengbao Jiang, Maria Lomeli, Patrick Lewis, Gautier Izacard, Edouard Grave, Sebastian Riedel, Fabio Petroni

Evaluation of text generation to date has primarily focused on content created sequentially, rather than improvements on a piece of text.

Text Generation

Atlas: Few-shot Learning with Retrieval Augmented Language Models

1 code implementation5 Aug 2022 Gautier Izacard, Patrick Lewis, Maria Lomeli, Lucas Hosseini, Fabio Petroni, Timo Schick, Jane Dwivedi-Yu, Armand Joulin, Sebastian Riedel, Edouard Grave

Retrieval augmented models are known to excel at knowledge intensive tasks without the need for as many parameters, but it is unclear whether they work in few-shot settings.

Fact Checking Few-Shot Learning +6

Improving Wikipedia Verifiability with AI

1 code implementation8 Jul 2022 Fabio Petroni, Samuel Broscheit, Aleksandra Piktus, Patrick Lewis, Gautier Izacard, Lucas Hosseini, Jane Dwivedi-Yu, Maria Lomeli, Timo Schick, Pierre-Emmanuel Mazaré, Armand Joulin, Edouard Grave, Sebastian Riedel

Hence, maintaining and improving the quality of Wikipedia references is an important challenge and there is a pressing need for better tools to assist humans in this effort.

Citation Recommendation Fact Checking

Masking schemes for universal marginalisers

no code implementations pproximateinference AABI Symposium 2019 Divya Gautam, Maria Lomeli, Kostis Gourgoulias, Daniel H. Thompson, Saurabh Johri

We consider the effect of structure-agnostic and structure-dependent masking schemes when training a universal marginaliser (arXiv:1711. 00695) in order to learn conditional distributions of the form $P(x_i |\mathbf x_{\mathbf b})$, where $x_i$ is a given random variable and $\mathbf x_{\mathbf b}$ is some arbitrary subset of all random variables of the generative model of interest.


Universal Marginaliser for Deep Amortised Inference for Probabilistic Programs

no code implementations16 Oct 2019 Robert Walecki, Kostis Gourgoulias, Adam Baker, Chris Hart, Chris Lucas, Max Zwiessele, Albert Buchard, Maria Lomeli, Yura Perov, Saurabh Johri

Probabilistic programming languages (PPLs) are powerful modelling tools which allow to formalise our knowledge about the world and reason about its inherent uncertainty.

Probabilistic Programming

Universal Marginalizer for Amortised Inference and Embedding of Generative Models

no code implementations12 Nov 2018 Robert Walecki, Albert Buchard, Kostis Gourgoulias, Chris Hart, Maria Lomeli, A. K. W. Navarro, Max Zwiessele, Yura Perov, Saurabh Johri

Probabilistic graphical models are powerful tools which allow us to formalise our knowledge about the world and reason about its inherent uncertainty.


Antithetic and Monte Carlo kernel estimators for partial rankings

no code implementations1 Jul 2018 Maria Lomeli, Mark Rowland, Arthur Gretton, Zoubin Ghahramani

We also present a novel variance reduction scheme based on an antithetic variate construction between permutations to obtain an improved estimator for the Mallows kernel.

Multi-Object Tracking Recommendation Systems

General Latent Feature Models for Heterogeneous Datasets

1 code implementation12 Jun 2017 Isabel Valera, Melanie F. Pradier, Maria Lomeli, Zoubin Ghahramani

Second, its Bayesian nonparametric nature allows us to automatically infer the model complexity from the data, i. e., the number of features necessary to capture the latent structure in the data.

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