Papers by Yatin Nandwani
Pointwise Mutual Information Based Metric and Decoding Strategy for Faithful Generation in Document Grounded Dialogs (2023.emnlp-main)
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| Challenge: | Existing metrics for faithfulness of response are not aligned with human judgments. |
| Approach: | They propose a new metric that utilizes (Conditional) Point-wise Mutual Information (PMI) between the generated response and the source document, conditioned on the dialogue. |
| Outcome: | The proposed metric improves on BEGIN benchmarks and shows that it generates more faithful responses than standard decoding techniques. |
Systematic Knowledge Injection into Large Language Models via Diverse Augmentation for Domain-Specific RAG (2025.findings-naacl)
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Kushagra Bhushan, Yatin Nandwani, Dinesh Khandelwal, Sonam Gupta, Gaurav Pandey, Dinesh Raghu, Sachindra Joshi
| Challenge: | Retrieval-Augmented Generation (RAG) enhances response relevance by incorporating retrieved domain knowledge in the context, retrieval errors can still lead to hallucinations and incorrect answers. |
| Approach: | They propose a framework that augments the learning process by context augmentation and knowledge paraphrasing by incorporating retrieved domain knowledge into the context. |
| Outcome: | The proposed framework achieves 10% relative gain in token-level recall while preserving the LLM’s generalization capabilities. |
Selective Self-to-Supervised Fine-Tuning for Generalization in Large Language Models (2025.findings-naacl)
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| Challenge: | Large Language Models (LLMs) can be fine-tuned on task-specific data to improve performance on target tasks but can be overfitted resulting in a loss of generalization. |
| Approach: | They propose a method that uses the correct model responses from a training set to fine-tune the model using the correct response and the gold response for the remaining samples. |
| Outcome: | The proposed approach reduces model specialization during the fine-tuning stage while improving generalization. |
Few shot chain-of-thought driven reasoning to prompt LLMs for open-ended medical question answering (2024.findings-emnlp)
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Saeel Nachane, Ojas Gramopadhye, Prateek Chanda, Ganesh Ramakrishnan, Kshitij Jadhav, Yatin Nandwani, Dinesh Raghu, Sachindra Joshi
| Challenge: | Large Language models (LLMs) are increasingly utilized in the healthcare sector for query-related tasks. |
| Approach: | They propose a modified version of the MedQA-USMLE dataset, named MEDQA-OPEN, which contains open-ended medical questions without options to mimic clinical scenarios. |
| Outcome: | The proposed approach outperforms the state-of-the-art 5-shot CoT-based prompt by exploring multiple differential diagnoses and narrowing down to a final diagnosis using MCQ-ELIMINATIVE. |