Papers by Shubham Gupta
Know Your RAG: Dataset Taxonomy and Generation Strategies for Evaluating RAG Systems (2025.coling-industry)
Copied to clipboard
Rafael Teixeira de Lima, Shubham Gupta, Cesar Berrospi Ramis, Lokesh Mishra, Michele Dolfi, Peter Staar, Panagiotis Vagenas
| Challenge: | Retrieval Augmented Generation (RAG) systems are widespread in the industry. |
| Approach: | They propose to use Q&A datasets to assess retrieval performance and label-targeted data generation to refine RAG datasets. |
| Outcome: | The proposed system can generate Q&A datasets with fine-tuned small LLMs. |
Deep Learning Based Named Entity Recognition Models for Recipes (2024.lrec-main)
Copied to clipboard
Ayush Agarwal, Janak Kapuriya, Shubham Agrawal, Akhil Vamshi Konam, Mansi Goel, Rishabh Gupta, Shrey Rastogi, Niharika Niharika, Ganesh Bagler
| Challenge: | Named entity recognition is a technique for extracting information from unstructured data with known labels. |
| Approach: | They use named entity recognition to annotate ingredients from recipe data . they use a clustering-based approach to annnotate 88,526 phrases . |
| Outcome: | The proposed method improves on a dataset of 88,526 phrases from RecipeDB . the fine-tuned spaCy-transformer performs better than the previous methods . |
Active2 Learning: Actively reducing redundancies in Active Learning methods for Sequence Tagging and Machine Translation (2021.naacl-main)
Copied to clipboard
| Challenge: | Existing approaches to deep learning for NLP require large amounts of labeled data. |
| Approach: | They propose an approach that iteratively selects a small number of examples for expert annotation based on their estimated utility in training the model. |
| Outcome: | The proposed approach reduces the data requirements of state-of-the-art AL strategies by 3-25% on multiple NLP tasks while achieving the same performance with virtually no additional computation overhead. |
Adapting Pretrained Text-to-Text Models for Long Text Sequences (2023.findings-emnlp)
Copied to clipboard
| Challenge: | Existing short-context models are limited in their domain coverage and can be used for long-sequence inputs. |
| Approach: | They propose to replace full attention in transformers with pooling-augmented blockwise attention and pretrain the model with a masked-span prediction task with spans of varying lengths. |
| Outcome: | The proposed model outperforms existing models on long-sequence summarization tasks and achieves competitive performance on long document corpora. |
Development of an Enterprise-Grade Contract Understanding System (2021.naacl-industry)
Copied to clipboard
Arvind Agarwal, Laura Chiticariu, Poornima Chozhiyath Raman, Marina Danilevsky, Diman Ghazi, Ankush Gupta, Shanmukha Guttula, Yannis Katsis, Rajasekar Krishnamurthy, Yunyao Li, Shubham Mudgal, Vitobha Munigala, Nicholas Phan, Dhaval Sonawane, Sneha Srinivasan, Sudarshan R. Thitte, Mitesh Vasa, Ramiya Venkatachalam, Vinitha Yaski, Huaiyu Zhu
| Challenge: | Currently, legal contract review remains an expensive and arduous process. |
| Approach: | They describe a commercial system designed and deployed for contract understanding that enables legal professionals to review contracts. |
| Outcome: | The proposed system is used by a wide range of enterprise users and solves three major challenges. |