Papers by Blaine Hill
Conversational Question Answering with Language Models Generated Reformulations over Knowledge Graph (2024.findings-acl)
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| Challenge: | State-of-the-art ConvQA methods struggle with inexplicit question-answer pairs, which can degrade ConvQ performance. |
| Approach: | They propose a reinforcement learning based model, CoRnNet, which utilizes question reformulations generated by large language models to improve ConvQA performance. |
| Outcome: | The proposed model outperforms state-of-the-art ConvQA models by using question reformulations generated by large language models (LLMs). |