Reimagining Retrieval Augmented Language Models for Answering Queries (2023.findings-acl)
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Wang-Chiew Tan, Yuliang Li, Pedro Rodriguez, Richard James, Xi Victoria Lin, Alon Halevy, Wen-tau Yih
| Challenge: | Large language models (LLMs) are expensive to train, deploy, and maintain, both financially and in terms of environmental impact. |
| Approach: | They present a reality check on large language models and compare their predictions to retrieval-augmented language models. |
| Outcome: | The proposed models fare better on question answering tasks and have become the foundation of impressive demos like Chat-GPT. |
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