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What is Boosting RALMs with In-Context Learning about?

This study presents an in-context learning approach to enhance the robustness of Retrieval-Augmented Language Models (RALMs) in open-domain question answering (QA) by improving their ability to identify unanswerable queries and conflicting information. By incorporating Machine Reading Comprehension (MRC) demonstrations, the proposed method boosts the reasoning capabilities of RALMs without requiring additional fine-tuning. Experiments demonstrate that this approach significantly increases accuracy in handli

Author

Aeyan Ashraf

Language

EN