Papers by Sravan Jayanthi

2 papers
Synthetic Multimodal Question Generation (2024.findings-emnlp)

Copied to clipboard

Challenge: Multimodal Retrieval Augmented Generation (MMRAG) is a powerful approach to question-answering over multimodal documents.
Approach: They propose a synthetic data generation framework that leverages interplay between a retriever, large language model and large multimodal model to generate question and answer pairs directly from multimodal documents.
Outcome: The proposed framework generates question and answer pairs from 1024 questions over Wikipedia documents and evaluates state-of-the-art models using it.
Retrieve and Copy: Scaling ASR Personalization to Large Catalogs (2023.emnlp-industry)

Copied to clipboard

Challenge: End-to-end ASR models struggle to recognize uncommon domain-specific words due to limited audio context.
Approach: They propose a "Retrieve and Copy" mechanism to improve latency while retaining the accuracy even when scaled to a large catalog.
Outcome: The proposed method achieves 6% more word error rate reduction and 3.6% improvement in F1 when scaled to a large catalog size while retaining the accuracy.

What is GenGO?

GenGO is an NLP powered publication search system. It currenctly indexes 30k+ papers from ACL Anthology, and implements multi-aspect summarization, semantic search, and more!

Information

About
Limitations