15 citations · 47 across the 14 of their papers we have counts for
4 papers · 1 filter
Re2G: Retrieve, Rerank, Generate
Michael Glass, Gaetano Rossiello, Md Faisal Mahbub Chowdhury +3
As demonstrated by GPT-3 and T5, transformers grow in capability as parameter spaces become larger and larger. However, for tasks that require a large amount of knowledge, non-para…
KGI: An Integrated Framework for Knowledge Intensive Language Tasks
Md Faisal Mahbub Chowdhury, Michael Glass, Gaetano Rossiello +2
In this paper, we present a system to showcase the capabilities of the latest state-of-the-art retrieval augmented generation models trained on knowledge-intensive language tasks,…
End-to-End Table Question Answering via Retrieval-Augmented Generation
Feifei Pan, Mustafa Canim, Michael Glass +2
Most existing end-to-end Table Question Answering (Table QA) models consist of a two-stage framework with a retriever to select relevant table candidates from a corpus and a reader…
Applying a Generic Sequence-to-Sequence Model for Simple and Effective Keyphrase Generation
Md Faisal Mahbub Chowdhury, Gaetano Rossiello, Michael Glass +2
In recent years, a number of keyphrase generation (KPG) approaches were proposed consisting of complex model architectures, dedicated training paradigms and decoding strategies. In…