Search Results for author:
Found 13 papers, 11 papers with code
XGLUE: A New Benchmark Datasetfor Cross-lingual Pre-training, Understanding and Generation
In this paper, we introduce XGLUE, a new benchmark dataset to train large-scale cross-lingual pre-trained models using multilingual and bilingual corpora, and evaluate their performance across a diverse set of cross-lingual tasks.
MS MARCO Web Search: a Large-scale Information-rich Web Dataset with Millions of Real Click Labels
Recent breakthroughs in large models have highlighted the critical significance of data scale, labels and modals.
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.
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)
SimANS: Simple Ambiguous Negatives Sampling for Dense Text Retrieval
Thus, we propose a simple ambiguous negatives sampling method, SimANS, which incorporates a new sampling probability distribution to sample more ambiguous negatives.
PROD: Progressive Distillation for Dense Retrieval
It is common that a better teacher model results in a bad student via distillation due to the nonnegligible gap between teacher and student.
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.
XGLUE: A New Benchmark Dataset for Cross-lingual Pre-training, Understanding and Generation
In this paper, we introduce XGLUE, a new benchmark dataset that can be used to train large-scale cross-lingual pre-trained models using multilingual and bilingual corpora and evaluate their performance across a diverse set of cross-lingual tasks.
MS MARCO: A Human Generated MAchine Reading COmprehension Dataset
The size of the dataset and the fact that the questions are derived from real user search queries distinguishes MS MARCO from other well-known publicly available datasets for machine reading comprehension and question-answering.
