Fabio Petroni

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NeurIPS 2020 EfficientQA Competition: Systems, Analyses and Lessons Learned

We review the EfficientQA competition from NeurIPS 2020. The competition...
01/01/2021 ∙ by Sewon Min, et al. ∙ 15 ∙ share

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Multi-task Retrieval for Knowledge-Intensive Tasks

Retrieving relevant contexts from a large corpus is a crucial step for t...
01/01/2021 ∙ by Jean Maillard, et al. ∙ 0 ∙ share

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A Memory Efficient Baseline for Open Domain Question Answering

Recently, retrieval systems based on dense representations have led to i...
12/30/2020 ∙ by Gautier Izacard, et al. ∙ 0 ∙ share

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Generating Fact Checking Briefs

Fact checking at scale is difficult – while the number of active fact ch...
11/10/2020 ∙ by Angela Fan, et al. ∙ 0 ∙ share

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Autoregressive Entity Retrieval

Entities are at the center of how we represent and aggregate knowledge. ...
10/02/2020 ∙ by Nicola De Cao, et al. ∙ 5 ∙ share

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KILT: a Benchmark for Knowledge Intensive Language Tasks

Challenging problems such as open-domain question answering, fact checki...
09/04/2020 ∙ by Fabio Petroni, et al. ∙ 6 ∙ share

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Video Understanding as Machine Translation

With the advent of large-scale multimodal video datasets, especially seq...
06/12/2020 ∙ by Bruno Korbar, et al. ∙ 11 ∙ share

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Concept Matching for Low-Resource Classification

We propose a model to tackle classification tasks in the presence of ver...
06/01/2020 ∙ by Federico Errica, et al. ∙ 7 ∙ share

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Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks

Large pre-trained language models have been shown to store factual knowl...
05/22/2020 ∙ by Patrick Lewis, et al. ∙ 0 ∙ share

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How Context Affects Language Models' Factual Predictions

When pre-trained on large unsupervised textual corpora, language models ...
05/10/2020 ∙ by Fabio Petroni, et al. ∙ 0 ∙ share

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Zero-shot Entity Linking with Dense Entity Retrieval

We consider the zero-shot entity-linking challenge where each entity is ...
11/10/2019 ∙ by Ledell Wu, et al. ∙ 0 ∙ share

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How Decoding Strategies Affect the Verifiability of Generated Text

Language models are of considerable importance. They are used for pretra...
11/09/2019 ∙ by Luca Massarelli, et al. ∙ 0 ∙ share

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Language Models as Knowledge Bases?

Recent progress in pretraining language models on large textual corpora ...
09/03/2019 ∙ by Fabio Petroni, et al. ∙ 0 ∙ share

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SAFE: Self-Attentive Function Embeddings for Binary Similarity

The binary similarity problem consists in determining if two functions a...
11/13/2018 ∙ by Luca Massarelli, et al. ∙ 0 ∙ share

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Unsupervised Features Extraction for Binary Similarity Using Graph Embedding Neural Networks

In this paper we consider the binary similarity problem that consists in...
10/23/2018 ∙ by Roberto Baldoni, et al. ∙ 0 ∙ share

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Fabio Petroni

Featured Co-authors

Jianfeng Gao

Sebastian Riedel

Luke Zettlemoyer

Hannaneh Hajishirzi

Douwe Kiela

Tim Rocktäschel

Xiaodong Liu

Lorenzo Torresani

Edouard Grave

Hao Cheng

Wen-tau Yih

NeurIPS 2020 EfficientQA Competition: Systems, Analyses and Lessons Learned

Multi-task Retrieval for Knowledge-Intensive Tasks

A Memory Efficient Baseline for Open Domain Question Answering

Generating Fact Checking Briefs

Autoregressive Entity Retrieval

KILT: a Benchmark for Knowledge Intensive Language Tasks

Video Understanding as Machine Translation

Concept Matching for Low-Resource Classification

Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks

How Context Affects Language Models' Factual Predictions

Zero-shot Entity Linking with Dense Entity Retrieval

How Decoding Strategies Affect the Verifiability of Generated Text

Language Models as Knowledge Bases?

SAFE: Self-Attentive Function Embeddings for Binary Similarity

Unsupervised Features Extraction for Binary Similarity Using Graph Embedding Neural Networks