465 citations · 1.3k across the 16 of their papers we have counts for
18 papers
Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context
Gemini Team, Petko Georgiev, Ving Ian Lei +1132
In this report, we introduce the Gemini 1.5 family of models, representing the next generation of highly compute-efficient multimodal models capable of recalling and reasoning over…
Querying Large Language Models with SQL
Mohammed Saeed, Nicola De Cao, Paolo Papotti
In many use-cases, information is stored in text but not available in structured data. However, extracting data from natural language text to precisely fit a schema, and thus enabl…
Sparse Interventions in Language Models with Differentiable Masking
Nicola De Cao, Leon Schmid, Dieuwke Hupkes +1
There has been a lot of interest in understanding what information is captured by hidden representations of language models (LMs). Typically, interpretation methods i) do not guara…
GenIE: Generative Information Extraction
Martin Josifoski, Nicola De Cao, Maxime Peyrard +2
Structured and grounded representation of text is typically formalized by closed information extraction, the problem of extracting an exhaustive set of (subject, relation, object)…
Highly Parallel Autoregressive Entity Linking with Discriminative Correction
Nicola De Cao, Wilker Aziz, Ivan Titov
Generative approaches have been recently shown to be effective for both Entity Disambiguation and Entity Linking (i.e., joint mention detection and disambiguation). However, the pr…
Editing Factual Knowledge in Language Models
Nicola De Cao, Wilker Aziz, Ivan Titov
The factual knowledge acquired during pre-training and stored in the parameters of Language Models (LMs) can be useful in downstream tasks (e.g., question answering or textual infe…
Multilingual Autoregressive Entity Linking
Nicola De Cao, Ledell Wu, Kashyap Popat +7
We present mGENRE, a sequence-to-sequence system for the Multilingual Entity Linking (MEL) problem -- the task of resolving language-specific mentions to a multilingual Knowledge B…
NeurIPS 2020 EfficientQA Competition: Systems, Analyses and Lessons Learned
Sewon Min, Jordan Boyd-Graber, Chris Alberti +50
We review the EfficientQA competition from NeurIPS 2020. The competition focused on open-domain question answering (QA), where systems take natural language questions as input and…
A Memory Efficient Baseline for Open Domain Question Answering
Gautier Izacard, Fabio Petroni, Lucas Hosseini +3
Recently, retrieval systems based on dense representations have led to important improvements in open-domain question answering, and related tasks. While very effective, this appro…
Autoregressive Entity Retrieval
Nicola De Cao, Gautier Izacard, Sebastian Riedel +1
Entities are at the center of how we represent and aggregate knowledge. For instance, Encyclopedias such as Wikipedia are structured by entities (e.g., one per Wikipedia article).…
Interpreting Graph Neural Networks for NLP With Differentiable Edge Masking
Michael Sejr Schlichtkrull, Nicola De Cao, Ivan Titov
Graph neural networks (GNNs) have become a popular approach to integrating structural inductive biases into NLP models. However, there has been little work on interpreting them, an…
The Power Spherical distribution
Nicola De Cao, Wilker Aziz
There is a growing interest in probabilistic models defined in hyper-spherical spaces, be it to accommodate observed data or latent structure. The von Mises-Fisher (vMF) distributi…
How do Decisions Emerge across Layers in Neural Models? Interpretation with Differentiable Masking
Nicola De Cao, Michael Schlichtkrull, Wilker Aziz +1
Attribution methods assess the contribution of inputs to the model prediction. One way to do so is erasure: a subset of inputs is considered irrelevant if it can be removed without…
Block Neural Autoregressive Flow
Nicola De Cao, Ivan Titov, Wilker Aziz
Normalising flows (NFS) map two density functions via a differentiable bijection whose Jacobian determinant can be computed efficiently. Recently, as an alternative to hand-crafted…
Question Answering by Reasoning Across Documents with Graph Convolutional Networks
Nicola De Cao, Wilker Aziz, Ivan Titov
Most research in reading comprehension has focused on answering questions based on individual documents or even single paragraphs. We introduce a neural model which integrates and…
Explorations in Homeomorphic Variational Auto-Encoding
Luca Falorsi, Pim de Haan, Tim R. Davidson +4
The manifold hypothesis states that many kinds of high-dimensional data are concentrated near a low-dimensional manifold. If the topology of this data manifold is non-trivial, a co…
MolGAN: An implicit generative model for small molecular graphs
Nicola De Cao, Thomas Kipf
Deep generative models for graph-structured data offer a new angle on the problem of chemical synthesis: by optimizing differentiable models that directly generate molecular graphs…
Hyperspherical Variational Auto-Encoders
Tim R. Davidson, Luca Falorsi, Nicola De Cao +2
The Variational Auto-Encoder (VAE) is one of the most used unsupervised machine learning models. But although the default choice of a Gaussian distribution for both the prior and p…