Papers by Himanshu Jain
DynamicTOC: Persona-based Table of Contents for Consumption of Long Documents (2022.naacl-main)
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Himanshu Maheshwari, Nethraa Sivakumar, Shelly Jain, Tanvi Karandikar, Vinay Aggarwal, Navita Goyal, Sumit Shekhar
| Challenge: | Long documents are tedious to read through and can be authored by multiple entities . traditional document navigation is through a Table of Contents (ToC) but there is no way to highlight information relevant to different personas. |
| Approach: | They propose a dynamic table of content-based navigator that highlights sections of interest . DYNAMICTOC is augmented with short questions to assist users in understanding underlying content . |
| Outcome: | The proposed navigator highlights sections of interest in documents as per the aspects relevant to different personas. human and automatic evaluations suggest the efficacy of both end-to-end pipeline and different components. |
Language Models Get a Gender Makeover: Mitigating Gender Bias with Few-Shot Data Interventions (2023.acl-short)
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| Challenge: | Existing approaches to de-bias pre-trained large language models focus on changes to training regime, but this is not feasible. |
| Approach: | They propose to de-bias a pre-trained model by fine-tuning it on only 10 examples . they show that the technique performs better than competitive baselines . |
| Outcome: | The proposed method performs better than competitive state-of-the-art baselines with minimal loss in language modeling ability. |
Semantic Label Smoothing for Sequence to Sequence Problems (2020.emnlp-main)
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Michal Lukasik, Himanshu Jain, Aditya Menon, Seungyeon Kim, Srinadh Bhojanapalli, Felix Yu, Sanjiv Kumar
| Challenge: | Existing methods for seq2seq regularization use label smoothing, but it is difficult to extend it to other datasets. |
| Approach: | They propose a method that smooths over well formed relevant sequences that are semantically similar to the target sequence. |
| Outcome: | The proposed method shows a consistent and significant improvement over the state-of-the-art methods on different datasets. |
UnityAI Guard: Pioneering Toxicity Detection Across Low-Resource Indian Languages (2025.emnlp-demos)
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Himanshu Beniwal, Reddybathuni Venkat, Rohit Kumar, Birudugadda Srivibhav, Daksh Jain, Pavan Deekshith Doddi, Eshwar Dhande, Adithya Ananth, null Kuldeep, Mayank Singh
| Challenge: | Existing systems target high-resource languages, but UnityAI-Guard addresses this gap by developing state-of-the-art models for binary toxicity classification targeting low-resourced Indian languages. |
| Approach: | They propose a framework for binary toxicity classification targeting low-resource Indian languages. |
| Outcome: | The proposed framework achieves an impressive average F1-score of 84.23% across seven languages, leveraging a dataset of 567k training instances and 30k manually verified test instances. |