Papers by Nirav Shah
Knowledge Extraction on Semi-Structured Content: Does It Remain Relevant for Question Answering in the Era of LLMs? (2026.eacl-long)
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Kai Sun, Yin Huang, Srishti Mehra, Mohammad Kachuee, Xilun Chen, Renjie Tao, Zhaojiang Lin, Andrea Jessee, Nirav Shah, Alex L Betty, Yue Liu, Anuj Kumar, Wen-tau Yih, Xin Luna Dong
| Challenge: | Existing literature on knowledge extraction for question answering questions whether it is still relevant for question answerrs. |
| Approach: | They extend an existing benchmark with knowledge extraction annotations and evaluate commercial and open-source LLMs of varying sizes. |
| Outcome: | The proposed model can achieve high QA accuracy, but can still benefit from knowledge extraction through augmentation with extracted triples and multi-task learning. |
PrismRAG: Boosting RAG Factuality with Distractor Resilience and Strategized Reasoning (2025.emnlp-industry)
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Mohammad Kachuee, Teja Gollapudi, Minseok Kim, Yin Huang, Kai Sun, Xiao Yang, Jiaqi Wang, Nirav Shah, Yue Liu, Aaron Colak, Anuj Kumar, Wen-tau Yih, Xin Luna Dong
| Challenge: | Existing methods to improve factuality of large language models (LLMs) rely on human-engineered instructions. |
| Approach: | They propose a retrieval-augmented generation framework that trains the model with distractor-aware QA pairs mixing gold evidence with subtle distractor passages and instills reasoning-centric habits that make the LLM plan, rationalize, and synthesize without extensive human engineered instructions. |
| Outcome: | The proposed framework outperforms state-of-the-art solutions across 12 open-book RAG QA benchmarks and is being deployed in production. |