Papers by Md Faisal Mahbub Chowdhury
KGI: An Integrated Framework for Knowledge Intensive Language Tasks (2022.emnlp-demos)
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Md Faisal Mahbub Chowdhury, Michael Glass, Gaetano Rossiello, Alfio Gliozzo, Nandana Mihindukulasooriya
| Challenge: | Existing state-of-the-art retrieval augmented generation models are not available for knowledge-intensive language tasks. |
| Approach: | They propose a retrieval augmented generation system that showcases the latest state-of-the-art retrieval models on knowledge-intensive language tasks. |
| Outcome: | The proposed system is based on the core of the KGI system. |
Re2G: Retrieve, Rerank, Generate (2022.naacl-main)
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Michael Glass, Gaetano Rossiello, Md Faisal Mahbub Chowdhury, Ankita Naik, Pengshan Cai, Alfio Gliozzo
| Challenge: | Recent models such as RAG and REALM incorporate retrieval into conditional generation. |
| Approach: | They propose a method that combines retrieval and reranking into a BART-based sequence-to-sequence generation. |
| Outcome: | The proposed model combines retrieval and reranking into a BART-based sequence-to-sequence generation. |
Robust Retrieval Augmented Generation for Zero-shot Slot Filling (2021.emnlp-main)
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| Challenge: | Automating high quality knowledge graphs from a given collection of documents remains a challenging problem in AI. |
| Approach: | They propose a novel approach to slot filling that extends dense passage retrieval with hard negatives and robust training procedures for retrieval augmented generation models. |
| Outcome: | The proposed model improves on both T-REx and zsRE slot filling datasets and ranks at the top-1 position in the KILT leaderboard. |