Papers by Riya Sawhney
Few-shot Transfer Learning for Knowledge Base Question Answering: Fusing Supervised Models with In-Context Learning (2024.acl-long)
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| Challenge: | Existing Knowledge Base Question Answering (KBQA) architectures are expensive and time-consuming to deploy. |
| Approach: | They propose a KBQA architecture that performs KB-retrieval using multiple source-trained retrievers and re-ranks using an LLM. |
| Outcome: | The proposed architecture outperforms adaptations of SoTA KBQA models when training data is limited. |
Iterative Repair with Weak Verifiers for Few-shot Transfer in KBQA with Unanswerability (2025.findings-acl)
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| Challenge: | Existing models for KBQA with unanswerable questions are inadequate for real-world applications. |
| Approach: | They propose a task of few-shot transfer for KBQA with unanswerable questions that extends FuSIC-KBQA to include feedback for unanswered questions. |
| Outcome: | The proposed model outperforms suitable adaptations of multiple LLM-based and supervised SoTA models on the task while establishing a new performance for answerable few-shot transfer as well. |