Papers by Sonam Gupta
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. |