Papers by Ryan Prenger
Context Generation Improves Open Domain Question Answering (2023.findings-eacl)
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Dan Su, Mostofa Patwary, Shrimai Prabhumoye, Peng Xu, Ryan Prenger, Mohammad Shoeybi, Pascale Fung, Anima Anandkumar, Bryan Catanzaro
| Challenge: | Existing closed-book question answering methods do not fully exploit the parameterized knowledge. |
| Approach: | They propose a closed-book QA framework which uses a coarse-to-fine approach to extract the relevant knowledge and answer a question. |
| Outcome: | The proposed method outperforms open-book QA methods on three QA benchmarks. |
Evaluating Parameter Efficient Learning for Generation (2022.emnlp-main)
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Peng Xu, Mostofa Patwary, Shrimai Prabhumoye, Virginia Adams, Ryan Prenger, Wei Ping, Nayeon Lee, Mohammad Shoeybi, Bryan Catanzaro
| Challenge: | Parameter efficient learning methods (PERMs) are gaining attention for their ability to adapt to a downstream task. |
| Approach: | They propose to use parameter efficient learning methods to improve model adaptation . they compare in-domain evaluations and generalizations to unseen domains and new datasets . |
| Outcome: | The proposed method outperforms finetuning and PERMs in in-domain evaluations. |
Multi-Stage Prompting for Knowledgeable Dialogue Generation (2022.findings-acl)
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Zihan Liu, Mostofa Patwary, Ryan Prenger, Shrimai Prabhumoye, Wei Ping, Mohammad Shoeybi, Bryan Catanzaro
| Challenge: | Existing knowledge-grounded dialogue systems typically use finetuned versions of a pretrained language model and large-scale knowledge bases. |
| Approach: | They propose a multi-stage prompting approach to generate knowledgeable responses from a single pretrained LM. |
| Outcome: | The proposed model outperforms the state-of-the-art retrieval-based model in terms of knowledge relevance and correctness by 5.8% and 5%, respectively. |