Papers by Mukund Rungta
Unified Contextual Query Rewriting (2023.acl-industry)
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Yingxue Zhou, Jie Hao, Mukund Rungta, Yang Liu, Eunah Cho, Xing Fan, Yanbin Lu, Vishal Vasudevan, Kellen Gillespie, Zeynab Raeesy
| Challenge: | Large-scale conversational AI agents such as Alexa, Siri, and Google Assistant are becoming increasingly popular in real-world applications to assist users in daily life. |
| Approach: | They propose a unified contextual query rewriting model that unifies QR for friction reduction and contextual carryover . they leverage the text-to-text unified framework which uses independent tasks with weighted loss to account for task importance . |
| Outcome: | The proposed model reduces friction and contextual carryover by using multiple auxiliary tasks. |
Forgotten Knowledge: Examining the Citational Amnesia in NLP (2023.acl-long)
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| Challenge: | a recent study examines how far back in time we tend to cite papers . citation patterns are correlated with age, age, and other factors . |
| Approach: | They analyze citation patterns across time and examine temporal changes . they find that 62% of cited papers are from the immediate five years prior to publication . |
| Outcome: | The authors show that citing papers is the primary method of scientific writing . they show that the trend has reversed and current papers have low temporal diversity . |
Alternate Preference Optimization for Unlearning Factual Knowledge in Large Language Models (2025.coling-main)
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Anmol Reddy Mekala, Vineeth Dorna, Shreya Dubey, Abhishek Lalwani, David Koleczek, Mukund Rungta, Sadid A. Hasan, Elita A.A Lobo
| Challenge: | Existing methods for large language models rely on negative feedback to suppress responses related to the forget set, which often results in nonsensical or inconsistent outputs, diminishing model utility and posing potential privacy risks. |
| Approach: | They propose an approach which combines negative feedback with in-domain positive feedback on the forget set and introduces new evaluation metrics to assess the quality of responses related to the forget sets. |
| Outcome: | The proposed approach avoids undesirable model behaviors while maintaining overall model performance. |
HiGen: Hierarchy-Aware Sequence Generation for Hierarchical Text Classification (2024.eacl-long)
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| Challenge: | Hierarchical text classification is a complex subtask under multi-label text classification . the relevance of document sections can vary based on the hierarchy level, necessitating a dynamic document representation. |
| Approach: | They propose a text-generation-based framework that uses language models to encode dynamic text representations. |
| Outcome: | The proposed framework surpasses existing methods while handling data and mitigating class imbalance. |
Geographic Citation Gaps in NLP Research (2022.emnlp-main)
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| Challenge: | a vast number of papers accepted at top NLP venues come from a handful of western countries and (lately) China. |
| Approach: | They ask researchers to examine the relationship between geographical location and publication success . they use a dataset of 70,000 papers from the ACL Anthology to examine their citation network . |
| Outcome: | The proposed dataset of 70,000 papers from the ACL Anthology shows that there are substantial geographical disparities in paper acceptance and citations . |