4k citations · 4.8k across the 18 of their papers we have counts for
19 papers
Subtraction Gets You More: Gap-Aware Retrieval for Multimodal Multi-Hop QA
Sunah O, Jay-Yoon Lee
In multimodal multi-hop question answering, we focus on the initial retrieval stage via two distinct tasks: (1) evidence set completion, retrieving missing evidence given context,…
The Troubling Emergence of Hallucination in Large Language Models -- An Extensive Definition, Quantification, and Prescriptive Remediations
Vipula Rawte, Swagata Chakraborty, Agnibh Pathak +5
The recent advancements in Large Language Models (LLMs) have garnered widespread acclaim for their remarkable emerging capabilities. However, the issue of hallucination has paralle…
LLaMA: Open and Efficient Foundation Language Models
Hugo Touvron, Thibaut Lavril, Gautier Izacard +11
We introduce LLaMA, a collection of foundation language models ranging from 7B to 65B parameters. We train our models on trillions of tokens, and show that it is possible to train…
Task-aware Retrieval with Instructions
Akari Asai, Timo Schick, Patrick Lewis +5
We study the problem of retrieval with instructions, where users of a retrieval system explicitly describe their intent along with their queries. We aim to develop a general-purpos…
EditEval: An Instruction-Based Benchmark for Text Improvements
Jane Dwivedi-Yu, Timo Schick, Zhengbao Jiang +6
Evaluation of text generation to date has primarily focused on content created sequentially, rather than improvements on a piece of text. Writing, however, is naturally an iterativ…
PEER: A Collaborative Language Model
Timo Schick, Jane Dwivedi-Yu, Zhengbao Jiang +7
Textual content is often the output of a collaborative writing process: We start with an initial draft, ask for suggestions, and repeatedly make changes. Agnostic of this process,…
Atlas: Few-shot Learning with Retrieval Augmented Language Models
Gautier Izacard, Patrick Lewis, Maria Lomeli +7
Large language models have shown impressive few-shot results on a wide range of tasks. However, when knowledge is key for such results, as is the case for tasks such as question an…
Improving Wikipedia Verifiability with AI
Fabio Petroni, Samuel Broscheit, Aleksandra Piktus +10
Verifiability is a core content policy of Wikipedia: claims that are likely to be challenged need to be backed by citations. There are millions of articles available online and tho…
Are Large-scale Datasets Necessary for Self-Supervised Pre-training?
Alaaeldin El-Nouby, Gautier Izacard, Hugo Touvron +3
Pre-training models on large scale datasets, like ImageNet, is a standard practice in computer vision. This paradigm is especially effective for tasks with small training sets, for…
The Web Is Your Oyster - Knowledge-Intensive NLP against a Very Large Web Corpus
Aleksandra Piktus, Fabio Petroni, Vladimir Karpukhin +8
In order to address increasing demands of real-world applications, the research for knowledge-intensive NLP (KI-NLP) should advance by capturing the challenges of a truly open-doma…
Unsupervised Dense Information Retrieval with Contrastive Learning
Gautier Izacard, Mathilde Caron, Lucas Hosseini +4
Recently, information retrieval has seen the emergence of dense retrievers, using neural networks, as an alternative to classical sparse methods based on term-frequency. These mode…
ResMLP: Feedforward networks for image classification with data-efficient training
Hugo Touvron, Piotr Bojanowski, Mathilde Caron +8
We present ResMLP, an architecture built entirely upon multi-layer perceptrons for image classification. It is a simple residual network that alternates (i) a linear layer in which…
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…
Distilling Knowledge from Reader to Retriever for Question Answering
Gautier Izacard, Edouard Grave
The task of information retrieval is an important component of many natural language processing systems, such as open domain question answering. While traditional methods were base…
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).…
Leveraging Passage Retrieval with Generative Models for Open Domain Question Answering
Gautier Izacard, Edouard Grave
Generative models for open domain question answering have proven to be competitive, without resorting to external knowledge. While promising, this approach requires to use models w…
Data-driven Estimation of Sinusoid Frequencies
Gautier Izacard, Sreyas Mohan, Carlos Fernandez-Granda
Frequency estimation is a fundamental problem in signal processing, with applications in radar imaging, underwater acoustics, seismic imaging, and spectroscopy. The goal is to esti…
A Learning-Based Framework for Line-Spectra Super-resolution
Gautier Izacard, Brett Bernstein, Carlos Fernandez-Granda
We propose a learning-based approach for estimating the spectrum of a multisinusoidal signal from a finite number of samples. A neural-network is trained to approximate the spectra…