Papers by Mayank Jobanputra
Unsupervised Question Answering for Fact-Checking (D19-66)
Copied to clipboard
| Challenge: | Recent Deep Learning (DL) models have achieved human-level accuracy on natural language tasks such as question-answering, natural language inference, and textual entailment. |
| Approach: | They propose an unsupervised question-answering based approach for a similar task, fact-checking. |
| Outcome: | The proposed approach achieves label accuracy of 80.2% on the development set and 80.25% on the test set. |
Retrieval-Augmented Modular Prompt Tuning for Low-Resource Data-to-Text Generation (2024.lrec-main)
Copied to clipboard
| Challenge: | Data-to-text generation methods are often limited by data sparsity and lack of training data. |
| Approach: | They propose a retrieval-augmented modular prompt tuning method that generates texts with few hallucinations from structured data inputs. |
| Outcome: | The proposed method generates texts with few hallucinations and achieves state-of-the-art performance on a dataset for drone handover message generation. |
Samanantar: The Largest Publicly Available Parallel Corpora Collection for 11 Indic Languages (2022.tacl-1)
Copied to clipboard
Gowtham Ramesh, Sumanth Doddapaneni, Aravinth Bheemaraj, Mayank Jobanputra, Raghavan AK, Ajitesh Sharma, Sujit Sahoo, Harshita Diddee, Mahalakshmi J, Divyanshu Kakwani, Navneet Kumar, Aswin Pradeep, Srihari Nagaraj, Kumar Deepak, Vivek Raghavan, Anoop Kunchukuttan, Pratyush Kumar, Mitesh Shantadevi Khapra
| Challenge: | We present Samanantar, the largest publicly available parallel corpora collection for Indic languages . based on existing corporative, there has been limited benefit for resource-poor languages despite the lack of parallel corporals and monolingual corporata. |
| Approach: | They compile 12.4 million sentence pairs from existing corpora and mine 37.4 million from the Web. |
| Outcome: | The proposed model outperforms existing models and benchmarks on public datasets. |
Tree-of-Quote Prompting Improves Factuality and Attribution in Multi-Hop and Medical Reasoning (2025.emnlp-main)
Copied to clipboard
Justin Xu, Yiming Li, Zizheng Zhang, Augustine Yui Hei Luk, Mayank Jobanputra, Samarth Oza, Ashley Murray, Meghana Reddy Kasula, Andrew Parker, David W Eyre
| Challenge: | Large language models (LLMs) produce fluent but factually incorrect outputs, a phenomenon commonly referred to as hallucination. |
| Approach: | They propose a Tree-of-Quote framework that decomposes complex questions into subquestions and generates quotes to support each step without retrieval. |
| Outcome: | Experiments on StrategyQA, 2WikiMultiHopQA, MuSiQue, MoreHopQ, and MedQA show that ToQ improves factuality and attribution over baselines. |