Search Results for author:
Found 14 papers, 10 papers with code
Multilingual E5 Text Embeddings: A Technical Report
This technical report presents the training methodology and evaluation results of the open-source multilingual E5 text embedding models, released in mid-2023.
Improving Text Embeddings with Large Language Models
In this paper, we introduce a novel and simple method for obtaining high-quality text embeddings using only synthetic data and less than 1k training steps.
Large Search Model: Redefining Search Stack in the Era of LLMs
Modern search engines are built on a stack of different components, including query understanding, retrieval, multi-stage ranking, and question answering, among others.
Allies: Prompting Large Language Model with Beam Search
With the advance of large language models (LLMs), the research field of LLM applications becomes more and more popular and the idea of constructing pipelines to accomplish complex tasks by stacking LLM API calls come true.
Inference with Reference: Lossless Acceleration of Large Language Models
We propose LLMA, an LLM accelerator to losslessly speed up Large Language Model (LLM) inference with references.
LEAD: Liberal Feature-based Distillation for Dense Retrieval
Knowledge distillation is often used to transfer knowledge from a strong teacher model to a relatively weak student model.
Text Embeddings by Weakly-Supervised Contrastive Pre-training
This paper presents E5, a family of state-of-the-art text embeddings that transfer well to a wide range of tasks.
Ranked #11 on
Only Connect Walls Dataset Task 1 (Grouping)
on OCW
(using extra training data)
LexMAE: Lexicon-Bottlenecked Pretraining for Large-Scale Retrieval
In large-scale retrieval, the lexicon-weighting paradigm, learning weighted sparse representations in vocabulary space, has shown promising results with high quality and low latency.
SimLM: Pre-training with Representation Bottleneck for Dense Passage Retrieval
It employs a simple bottleneck architecture that learns to compress the passage information into a dense vector through self-supervised pre-training.
Less is Less: When Are Snippets Insufficient for Human vs Machine Relevance Estimation?
This raises questions about when such summaries are sufficient for relevance estimation by the ranking model or the human assessor, and whether humans and machines benefit from the document's full text in similar ways.
xMoCo: Cross Momentum Contrastive Learning for Open-Domain Question Answering
Dense passage retrieval has been shown to be an effective approach for information retrieval tasks such as open domain question answering.
Embedding-based Retrieval in Facebook Search
In this paper, we discuss the techniques for applying EBR to a Facebook Search system.
Web-Scale Responsive Visual Search at Bing
In this paper, we introduce a web-scale general visual search system deployed in Microsoft Bing.
CleanNet: Transfer Learning for Scalable Image Classifier Training with Label Noise
We demonstrate the effectiveness of the proposed algorithm on both of the label noise detection task and the image classification on noisy data task on several large-scale datasets.
Ranked #2 on
Image Classification
on Food-101N
(using extra training data)
