14 citations · 19 across the 17 of their papers we have counts for
7 papers
Quick Dense Retrievers Consume KALE: Post Training Kullback Leibler Alignment of Embeddings for Asymmetrical dual encoders
Daniel Campos, Alessandro Magnani, ChengXiang Zhai
In this paper, we consider the problem of improving the inference latency of language model-based dense retrieval systems by introducing structural compression and model size asymm…
Measuring the Effect of Influential Messages on Varying Personas
Chenkai Sun, Jinning Li, Hou Pong Chan +2
Predicting how a user responds to news events enables important applications such as allowing intelligent agents or content producers to estimate the effect on different communitie…
Noise-Robust Dense Retrieval via Contrastive Alignment Post Training
Daniel Campos, ChengXiang Zhai, Alessandro Magnani
The success of contextual word representations and advances in neural information retrieval have made dense vector-based retrieval a standard approach for passage and document rank…
To Asymmetry and Beyond: Structured Pruning of Sequence to Sequence Models for Improved Inference Efficiency
Daniel Campos, ChengXiang Zhai
Sequence-to-sequence language models can be used to produce abstractive summaries which are coherent, relevant, and concise. Still, model sizes can make deployment in latency-sensi…
Dense Sparse Retrieval: Using Sparse Language Models for Inference Efficient Dense Retrieval
Daniel Campos, ChengXiang Zhai
Vector-based retrieval systems have become a common staple for academic and industrial search applications because they provide a simple and scalable way of extending the search to…
CONCRETE: Improving Cross-lingual Fact-checking with Cross-lingual Retrieval
Kung-Hsiang Huang, ChengXiang Zhai, Heng Ji
Fact-checking has gained increasing attention due to the widespread of falsified information. Most fact-checking approaches focus on claims made in English only due to the data sca…