Search Results for author:
Found 19 papers, 14 papers with code
LLaMA: Open and Efficient Foundation Language Models
We introduce LLaMA, a collection of foundation language models ranging from 7B to 65B parameters.
Ranked #3 on
Question Answering
on OBQA
Task-aware Retrieval with Instructions
We study the problem of retrieval with instructions, where users of a retrieval system explicitly describe their intent along with their queries.
EditEval: An Instruction-Based Benchmark for Text Improvements
Evaluation of text generation to date has primarily focused on content created sequentially, rather than improvements on a piece of text.
PEER: A Collaborative Language Model
Textual content is often the output of a collaborative writing process: We start with an initial draft, ask for suggestions, and repeatedly make changes.
Atlas: Few-shot Learning with Retrieval Augmented Language Models
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.
Ranked #1 on
Question Answering
on Natural Questions
Improving Wikipedia Verifiability with AI
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.
Are Large-scale Datasets Necessary for Self-Supervised Pre-training?
Our study shows that denoising autoencoders, such as BEiT or a variant that we introduce in this paper, are more robust to the type and size of the pre-training data than popular self-supervised methods trained by comparing image embeddings. We obtain competitive performance compared to ImageNet pre-training on a variety of classification datasets, from different domains.
The Web Is Your Oyster -- Knowledge-Intensive NLP against a Very Large Web Corpus
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-domain environment: web-scale knowledge, lack of structure, inconsistent quality and noise.
Unsupervised Dense Information Retrieval with Contrastive Learning
In this work, we explore the limits of contrastive learning as a way to train unsupervised dense retrievers and show that it leads to strong performance in various retrieval settings.
Contrastive Pre-training for Zero-Shot Information Retrieval
By contrast, in many other NLP tasks, conventional self-supervised pre-training based on masking leads to strong generalization with small number of training examples.
ResMLP: Feedforward networks for image classification with data-efficient training
We present ResMLP, an architecture built entirely upon multi-layer perceptrons for image classification.
Ranked #1 on
Image Classification
on Certificate Verification
NeurIPS 2020 EfficientQA Competition: Systems, Analyses and Lessons Learned
We review the EfficientQA competition from NeurIPS 2020.
A Memory Efficient Baseline for Open Domain Question Answering
Recently, retrieval systems based on dense representations have led to important improvements in open-domain question answering, and related tasks.
Distilling Knowledge from Reader to Retriever for Question Answering
A challenge of using such methods is to obtain supervised data to train the retriever model, corresponding to pairs of query and support documents.
Ranked #9 on
Question Answering
on NarrativeQA
Autoregressive Entity Retrieval
For instance, Encyclopedias such as Wikipedia are structured by entities (e. g., one per Wikipedia article).
Ranked #1 on
Entity Linking
on Derczynski
Leveraging Passage Retrieval with Generative Models for Open Domain Question Answering
Generative models for open domain question answering have proven to be competitive, without resorting to external knowledge.
Ranked #1 on
Question Answering
on ConditionalQA
Lossless Data Compression with Transformer
It is closely related to the problem of online learning of language models.
Data-driven Estimation of Sinusoid Frequencies
Frequency estimation is a fundamental problem in signal processing, with applications in radar imaging, underwater acoustics, seismic imaging, and spectroscopy.
A Learning-Based Framework for Line-Spectra Super-resolution
We propose a learning-based approach for estimating the spectrum of a multisinusoidal signal from a finite number of samples.
