Search Results for author:
Found 10 papers, 1 papers with code
Multi-stage Training with Improved Negative Contrast for Neural Passage Retrieval
In the context of neural passage retrieval, we study three promising techniques: synthetic data generation, negative sampling, and fusion.
Transforming LLMs into Cross-modal and Cross-lingual Retrieval Systems
Our multi-modal LLM-based retrieval system is capable of matching speech and text in 102 languages despite only training on 21 languages.
Gecko: Versatile Text Embeddings Distilled from Large Language Models
On the Massive Text Embedding Benchmark (MTEB), Gecko with 256 embedding dimensions outperforms all existing entries with 768 embedding size.
SamToNe: Improving Contrastive Loss for Dual Encoder Retrieval Models with Same Tower Negatives
Dual encoders have been used for retrieval tasks and representation learning with good results.
PaLM 2 Technical Report
Through extensive evaluations on English and multilingual language, and reasoning tasks, we demonstrate that PaLM 2 has significantly improved quality on downstream tasks across different model sizes, while simultaneously exhibiting faster and more efficient inference compared to PaLM.
Ranked #1 on
Question Answering
on StrategyQA
Self-Supervised Learning for Pairwise Data Refinement
We evaluate our methods on de-noising parallel texts and training neural machine translation models.
Neural Passage Retrieval with Improved Negative Contrast
In this paper we explore the effects of negative sampling in dual encoder models used to retrieve passages for automatic question answering.
Multilingual Universal Sentence Encoder for Semantic Retrieval
We introduce two pre-trained retrieval focused multilingual sentence encoding models, respectively based on the Transformer and CNN model architectures.
Effective Parallel Corpus Mining using Bilingual Sentence Embeddings
This paper presents an effective approach for parallel corpus mining using bilingual sentence embeddings.
