Papers by Saumajit Saha
DPL: Diverse Preference Learning Without A Reference Model (2025.naacl-long)
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Abhijnan Nath, Andrey Volozin, Saumajit Saha, Albert Aristotle Nanda, Galina Grunin, Rahul Bhotika, Nikhil Krishnaswamy
| Challenge: | Existing methods to direct preference alignment do not utilize diversity in preference annotations which limits their applicability. |
| Approach: | They propose a reference-model-free method that learns a baseline desirability in LLM responses while being robust to the diversity of preference annotations. |
| Outcome: | The proposed method learns a baseline desirability in LLM responses while being robust to the diversity of preference annotations. |
A Platform for Event Extraction in Hindi (2020.lrec-1)
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| Challenge: | Event Extraction is an important task in the widespread field of NLP, but there is no benchmark setup in Hindi. |
| Approach: | They propose an Event Extraction framework for Hindi language and develop deep learning based models to set as the baselines. |
| Outcome: | The proposed framework crawls more than seventeen hundred disaster related Hindi news articles from various news sources. |